diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py index be9fb7e..923b47e 100644 --- a/gnomad_toolbox/analysis/general.py +++ b/gnomad_toolbox/analysis/general.py @@ -5,7 +5,7 @@ import hail as hl from gnomad.assessment.summary_stats import freq_bin_expr -from gnomad_toolbox.load_data import _get_gnomad_release +from gnomad_toolbox.load_data import _get_dataset def get_variant_count_by_freq_bin( @@ -33,12 +33,12 @@ def get_variant_count_by_freq_bin( :param singletons: Include singletons. :param doubletons: Include doubletons. :param pass_only: Include only PASS variants. - :param kwargs: Keyword arguments to pass to _get_gnomad_release. Includes - 'ht', 'data_type', and 'version'. + :param kwargs: Keyword arguments to pass to `_get_dataset`. Includes 'ht', + 'data_type', and 'version'. :return: Dictionary with counts. """ # Load the Hail Table if not provided - ht = _get_gnomad_release(dataset="variant", **kwargs) + ht = _get_dataset(dataset="variant", **kwargs) # Filter to PASS variants. if pass_only: diff --git a/gnomad_toolbox/filtering/constraint.py b/gnomad_toolbox/filtering/constraint.py index edbb4ee..c9d3ec7 100644 --- a/gnomad_toolbox/filtering/constraint.py +++ b/gnomad_toolbox/filtering/constraint.py @@ -1 +1,103 @@ +"""Functions to filter gnomAD sites HT by constraint metrics.""" + +import hail as hl + +from gnomad_toolbox.filtering.vep import ( + filter_to_high_confidence_loftee, + get_gene_intervals, +) +from gnomad_toolbox.load_data import ( + CONSTRAINT_DATA, + _get_dataset, + get_compatible_dataset_versions, +) + + +def get_observed_plofs_for_gene_constraint( + gene_symbol: str, + version: str = None, + variant_ht: hl.Table = None, + coverage_ht: hl.Table = None, +) -> hl.Table: + """ + Filter to observed pLoF variants used for gene constraint metrics. + + The pLOF variant count displayed on the browser meets the following requirements: + + - PASS variant QC + - SNV + - Allele frequency ≤ 0.1% + - High-confidence LOFTEE in the MANE Select or Canonical transcript + - ≥ a specified coverage threshold (depends on the version) + + :param gene_symbol: Gene symbol. + :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD + session version. + :param variant_ht: Optional Hail Table with variants. If not provided, uses the + exome variant Table for the gnomAD session version. + :param coverage_ht: Optional Hail Table with coverage data. If not provided, uses + the exome coverage Table for the gnomAD session version. + :return: Table with pLoF variants. + """ + if variant_ht is not None and coverage_ht is None: + raise ValueError("Variant Hail Table provided without coverage Hail Table.") + + if coverage_ht is not None and variant_ht is None: + raise ValueError("Coverage Hail Table provided without variant Hail Table.") + + # Load the variant exomes Hail Table if not provided. + variant_ht = _get_dataset( + dataset="variant", + ht=variant_ht, + data_type="exomes", + version=version, + ) + + # Determine the coverage version compatible with the variant version. + coverage_version = get_compatible_dataset_versions("coverage", version, "exomes") + + # Load the coverage Hail Table if not provided. + coverage_ht = _get_dataset( + dataset="coverage", + ht=coverage_ht, + data_type="exomes", + version=coverage_version, + ) + + # Get gene intervals and filter tables. + gencode_version = get_compatible_dataset_versions("gencode", version) + intervals = get_gene_intervals(gene_symbol, gencode_version=gencode_version) + variant_ht = hl.filter_intervals(variant_ht, intervals) + coverage_ht = hl.filter_intervals(coverage_ht, intervals) + + # Determine constraint filters. + constraint_version = get_compatible_dataset_versions("constraint", version) + constraint_info = CONSTRAINT_DATA[constraint_version] + cov_field = constraint_info["exome_coverage_field"] + cov_cutoff = constraint_info["exome_coverage_cutoff"] + af_cutoff = constraint_info["af_cutoff"] + + # Annotate the exome coverage. + variant_ht = variant_ht.annotate( + exome_coverage=coverage_ht[variant_ht.locus][cov_field] + ) + + # Apply constraint filters. + variant_ht = variant_ht.filter( + (hl.len(variant_ht.filters) == 0) + & (hl.is_snp(variant_ht.alleles[0], variant_ht.alleles[1])) + & (variant_ht.freq[0].AF <= af_cutoff) + & (variant_ht.exome_coverage >= cov_cutoff) + ) + + # Filter to high-confidence LOFTEE variants. + variant_ht = filter_to_high_confidence_loftee( + gene_symbol=gene_symbol, + ht=variant_ht, + canonical_only=True, + ) + + return variant_ht + + # noqa: D104, D100 diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py index c5a008b..d08e253 100644 --- a/gnomad_toolbox/filtering/frequency.py +++ b/gnomad_toolbox/filtering/frequency.py @@ -6,7 +6,7 @@ from gnomad.utils.filtering import filter_arrays_by_meta from gnomad_toolbox.filtering.variant import get_single_variant -from gnomad_toolbox.load_data import _get_gnomad_release +from gnomad_toolbox.load_data import _get_dataset def get_ancestry_callstats( @@ -19,11 +19,11 @@ def get_ancestry_callstats( :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of ancestry groups. - :param kwargs: Keyword arguments to pass to _get_gnomad_release. + :param kwargs: Keyword arguments to pass to _get_dataset. :return: Table with callstats for the given ancestry groups and variant. """ # Load the Hail Table if not provided - ht = _get_gnomad_release(dataset="variant", **kwargs) + ht = _get_dataset(dataset="variant", **kwargs) # Check if gen_ancs is a single ancestry group. one_anc = isinstance(gen_ancs, str) diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py index 6705546..db1f5e3 100644 --- a/gnomad_toolbox/filtering/variant.py +++ b/gnomad_toolbox/filtering/variant.py @@ -8,7 +8,7 @@ from gnomad.utils.parse import parse_variant from gnomad.utils.reference_genome import get_reference_genome -from gnomad_toolbox.load_data import _get_gnomad_release +from gnomad_toolbox.load_data import _get_dataset def get_single_variant( @@ -35,7 +35,7 @@ def get_single_variant( :param position: Variant position. Required if `variant` is not provided. :param ref: Reference allele. Required if `variant` is not provided. :param alt: Alternate allele. Required if `variant` is not provided. - :param kwargs: Additional arguments to pass to `_get_gnomad_release`. + :param kwargs: Additional arguments to pass to `_get_dataset`. :return: Table with the single variant. """ if not variant and not all([contig, position, ref, alt]): @@ -45,7 +45,7 @@ def get_single_variant( ) # Load the Hail Table if not provided - ht = _get_gnomad_release(dataset="variant", **kwargs) + ht = _get_dataset(dataset="variant", **kwargs) # Determine the reference genome build for the ht. build = get_reference_genome(ht.locus).name @@ -71,7 +71,6 @@ def get_single_variant( def filter_by_intervals( intervals: Union[str, list[str]], - padding_bp: int = 0, **kwargs, ) -> hl.Table: """ @@ -80,17 +79,15 @@ def filter_by_intervals( :param intervals: Interval string or list of interval strings. The interval string format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or "chr1:1000-2000" (GRCh38). - :param padding_bp: Number of base pairs to pad the intervals. Default is 0bp. - :param kwargs: Arguments to pass to `_get_gnomad_release`. + :param kwargs: Arguments to pass to `_get_dataset`. :return: Table with variants in the interval(s). """ - # Load the Hail Table if not provided. - ht = _get_gnomad_release(dataset="variant", **kwargs) + # Load the Hail Table if not provided + ht = _get_dataset(dataset="variant", **kwargs) return interval_filter( ht, intervals, - padding_bp=padding_bp, reference_genome=get_reference_genome(ht.locus).name, ) @@ -108,11 +105,11 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl. :param gene: Gencode gene symbol. :param exon_padding_bp: Number of base pairs to pad the intervals. Default is 75bp. - :param kwargs: Arguments to pass to `_get_gnomad_release`. + :param kwargs: Arguments to pass to `_get_dataset`. :return: Table with variants in the specified gene. """ # Load the Hail Table if not provided. - ht = _get_gnomad_release(dataset="variant", **kwargs) + ht = _get_dataset(dataset="variant", **kwargs) # The gnomAD browser will display variants in CDS regions if present, otherwise UTR, # and finally exons. diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py index 049e5db..018c43b 100644 --- a/gnomad_toolbox/filtering/vep.py +++ b/gnomad_toolbox/filtering/vep.py @@ -1,9 +1,16 @@ """Functions to filter gnomAD sites HT by VEP annotations.""" +from typing import List, Optional + import hail as hl -from gnomad.utils.vep import LOF_CSQ_SET, filter_vep_transcript_csqs_expr +from gnomad.utils.filtering import filter_gencode_ht +from gnomad.utils.vep import ( + LOF_CSQ_SET, + filter_vep_transcript_csqs, + filter_vep_transcript_csqs_expr, +) -from gnomad_toolbox.load_data import _get_gnomad_release +from gnomad_toolbox.load_data import _get_dataset, get_compatible_dataset_versions # TODO: Check these csq sets, the ones in the code don't match what is listed on the @@ -76,7 +83,7 @@ def filter_by_consequence_category( :param synonymous: Whether to include synonymous variants. :param other: Whether to include other variants. :param pass_filters: Boolean if the variants pass the filters. - :param kwargs: Arguments to pass to _get_gnomad_release. + :param kwargs: Arguments to pass to `_get_dataset`. :return: Table with variants with the specified consequences. """ if not any([plof, missense, synonymous, other]): @@ -85,7 +92,7 @@ def filter_by_consequence_category( ) # Load the Hail Table if not provided - ht = _get_gnomad_release(dataset="variant", **kwargs) + ht = _get_dataset(dataset="variant", **kwargs) lof_csqs = list(LOF_CSQ_SET) missense_csqs = ["missense_variant", "inframe_insertion", "inframe_deletion"] @@ -116,23 +123,68 @@ def filter_by_consequence_category( return ht.filter(filter_expr) -# TODO: The following was in one of the notebooks, and I think we should add a wrapper -# around this function to make it much simpler instead of using it in the notebook. +def get_gene_intervals( + gene_symbol: str, gencode_version: Optional[str] = None +) -> List[hl.utils.Interval]: + """ + Get the GENCODE genomic intervals for a given gene symbol. + + :param gene_symbol: Gene symbol. + :param gencode_version: Optional GENCODE version. If not provided, uses the gencode + version associated with the gnomAD session. + :return: List of GENCODE intervals for the specified gene. + """ + # Load the Hail Table if not provided. + ht = _get_dataset(dataset="gencode", version=gencode_version) + gene_symbol = gene_symbol.upper() -# Filter to LOFTEE high-confidence variants for certain genes + intervals = filter_gencode_ht(gencode_ht=ht, feature="gene", genes=gene_symbol) + intervals = intervals.interval.collect() -# In this example, we are filtering to variants in ASH1L that are LOFTEE high-confidence -# (with no flags) in the MANE select transcript. + if not intervals: + raise ValueError(f"No interval found for gene: {gene_symbol}") -# from gnomad.utils.vep import filter_vep_transcript_csqs -# ht = get_gnomad_release(data_type='exomes', version='4.1') -# ht = filter_vep_transcript_csqs( -# ht, -# synonymous=False, -# mane_select=True, -# genes=["ASH1L"], -# match_by_gene_symbol=True, -# additional_filtering_criteria=[lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)], -# ) -# ht.show() -# ht.count() + return intervals + + +def filter_to_high_confidence_loftee( + gene_symbol: Optional[str] = None, + no_lof_flags: bool = False, + mane_select_only: bool = False, + canonical_only: bool = False, + version: Optional[str] = None, + **kwargs, +) -> hl.Table: + """ + Filter gnomAD variants to high-confidence LOFTEE variants for a gene. + + :param gene_symbol: Optional gene symbol to filter by. + :param no_lof_flags: Whether to exclude variants with LOFTEE flags. Default is + False. + :param mane_select_only: Whether to include only MANE Select transcripts. Default + is False. + :param canonical_only: Whether to include only canonical transcripts. Default is + False. + :param kwargs: Additional arguments to pass to `_get_dataset`. + :return: Table with high-confidence LOFTEE variants. + """ + # Load the Hail Table if not provided. + ht = _get_dataset(dataset="variant", version=version, **kwargs) + gene_symbol = gene_symbol.upper() if gene_symbol else None + + if gene_symbol: + gencode_version = get_compatible_dataset_versions("gencode", version) + ht = hl.filter_intervals( + ht, get_gene_intervals(gene_symbol, gencode_version=gencode_version) + ) + + return filter_vep_transcript_csqs( + ht, + synonymous=False, + canonical=canonical_only, + mane_select=mane_select_only, + genes=[gene_symbol], + match_by_gene_symbol=True, + loftee_labels=["HC"], + no_lof_flags=no_lof_flags, + ) diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py index de29e85..d02af7b 100644 --- a/gnomad_toolbox/load_data.py +++ b/gnomad_toolbox/load_data.py @@ -1,50 +1,117 @@ """Functions to import gnomAD data.""" -import functools -from typing import Optional +from typing import Callable, Optional, Union -import gnomad.resources.grch37.gnomad as grch37_gnomad -import gnomad.resources.grch38.gnomad as grch38_gnomad +import gnomad.resources.grch37 as grch37_res +import gnomad.resources.grch38 as grch38_res import hail as hl +from gnomad.resources.resource_utils import VersionedTableResource -GNOMAD_BY_BUILD = { - "GRCh37": grch37_gnomad, - "GRCh38": grch38_gnomad, +DATA_TYPES = ["exomes", "genomes", "joint"] +PEXT_DATA_TYPES = ["base_level", "annotation_level"] +RESOURCES_BY_BUILD = { + "GRCh37": grch37_res, + "GRCh38": grch38_res, } -DATASETS = { - "variant": "public_release", - "all_sites_an": "all_sites_an", - "coverage": "coverage", +# In the toolbox, for each dataset, we only support loading the most recent versions +# for each reference genome build (GRCh37 and GRCh38) and data type (exomes, genomes, +# joint). Older versions are not supported because they are less complete datasets, and +# some may have errors that have been fixed in newer versions. If other versions are +# actually needed, they can be loaded using `gnomad_methods` or directly loading the +# Table with hl.read_table. +VARIANT_DATA = { + "2.1.1": { + "reference_genome": "GRCh37", + "data_types": ["exomes", "genomes"], + "dataset_versions": { + "vep": "85", + "gencode": "v19", + "coverage": { + "exomes": "2.1", + "genomes": "2.1", + }, + "constraint": "2.1.1", + "pext": "v7", + "liftover": "2.1.1", + }, + }, + "4.1": { + "reference_genome": "GRCh38", + "data_types": ["exomes", "genomes", "joint"], + "dataset_versions": { + "vep": "105", + "gencode": "v39", + "coverage": {"exomes": "4.0", "genomes": "3.0.1"}, + "all_sites_an": "4.1", + "constraint": "4.1", + "pext": "v10", + "browser": "4.1", + }, + }, } -DATA_TYPES = ["exomes", "genomes", "joint"] -RELEASES_GLOBAL = { - "variant": { - "exomes": "EXOME_RELEASES", - "genomes": "GENOME_RELEASES", - "joint": "JOINT_RELEASES", +COVERAGE_DATA = { + "2.1": {"reference_genome": "GRCh37", "data_types": ["exomes", "genomes"]}, + "3.0.1": {"reference_genome": "GRCh38", "data_types": ["genomes"]}, + "4.0": { + "reference_genome": "GRCh38", + "data_types": ["exomes"], }, - "all_sites_an": { - "exomes": "EXOME_AN_RELEASES", - "genomes": "GENOME_AN_RELEASES", +} +ALL_SITES_AN_DATA = { + "4.1": { + "reference_genome": "GRCh38", + "data_types": ["exomes", "genomes"], + } +} +CONSTRAINT_DATA = { + "2.1.1": { + "reference_genome": "GRCh37", + "exome_coverage_field": "median", + "exome_coverage_cutoff": 30, + "af_cutoff": 0.001, }, - "coverage": { - "exomes": "EXOME_COVERAGE_RELEASES", - "genomes": "GENOME_COVERAGE_RELEASES", + "4.1": { + "reference_genome": "GRCh38", + "exome_coverage_field": "median_approx", + "exome_coverage_cutoff": 30, + "af_cutoff": 0.001, }, } -RELEASES = { - dataset: { - data_type: { - build: ( - None - if release_global.get(data_type) is None - else getattr(res, release_global.get(data_type), None) - ) - for build, res in GNOMAD_BY_BUILD.items() - } - for data_type in DATA_TYPES +LIFTOVER_DATA = { + "2.1.1": { + "reference_genome": "GRCh37", + "data_types": ["exomes", "genomes"], } - for dataset, release_global in RELEASES_GLOBAL.items() +} +PEXT_DATA = { + "v7": {"reference_genome": "GRCh37", "data_types": PEXT_DATA_TYPES}, + "v10": {"reference_genome": "GRCh38", "data_types": PEXT_DATA_TYPES}, +} +BROWSER_DATA = {"4.1": {"reference_genome": "GRCh38"}} +SUPPORTED_DATASETS = { + "variant": {"resource": "public_release", "versions": VARIANT_DATA}, + "coverage": {"resource": "coverage", "versions": COVERAGE_DATA}, + "all_sites_an": {"resource": "all_sites_an", "versions": ALL_SITES_AN_DATA}, + "constraint": {"resource": "constraint", "versions": CONSTRAINT_DATA}, + "liftover": {"resource": "liftover", "versions": LIFTOVER_DATA}, + "pext": {"resource": "pext", "versions": PEXT_DATA}, + "browser": {"resource": "browser_variant", "versions": BROWSER_DATA}, +} +SUPPORTED_REFERENCE_DATA = { + "vep": { + "resource": "vep_context", + "versions": { + "85": {"reference_genome": "GRCh37"}, + "105": {"reference_genome": "GRCh38"}, + }, + }, + "gencode": { + "resource": "gencode", + "versions": { + "v19": {"reference_genome": "GRCh37"}, + "v39": {"reference_genome": "GRCh38"}, + }, + }, } @@ -63,7 +130,8 @@ def __init__(self) -> None: :return: None. """ self.data_type = "exomes" - self.version = grch38_gnomad.CURRENT_EXOME_RELEASE + self.version = "4.1" + self.set_default_data() def set_default_data( self, @@ -80,36 +148,28 @@ def set_default_data( data_type = data_type or self.data_type version = version or self.version - # Validate data type. - if data_type and data_type not in DATA_TYPES: + # Validate version. + if version not in VARIANT_DATA: raise ValueError( - f"Data type {data_type} is invalid. Choose from {DATA_TYPES}" + f"Version {version} is not a supported gnomAD version in the Toolbox." ) - # Get all possible versions. - possible_versions = functools.reduce( - lambda x, y: (x or []) + (y or []), - [ - ds[dt][r] - for ds in RELEASES.values() - for dt in ([data_type] if data_type else DATA_TYPES) - for r in GNOMAD_BY_BUILD - ], - ) - - # Check version availability. - if version not in possible_versions: + # Validate data type for the version. + version_info = VARIANT_DATA[version] + if data_type not in version_info["data_types"]: raise ValueError(f"Version {version} for {data_type} is not available.") self.data_type = data_type self.version = version + self.reference_genome = version_info["reference_genome"] + self.compatible_datasets = version_info["dataset_versions"] # Global gnomad session object gnomad_session = GnomADSession() -def _get_gnomad_release( +def _get_dataset( ht: hl.Table = None, dataset: str = "variant", data_type: str = None, @@ -119,8 +179,8 @@ def _get_gnomad_release( Get gnomAD HT using a Hail Table, specific parameters, or session defaults. :param ht: Pre-loaded Hail Table. If provided, other parameters are ignored. - :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default - is variant. + :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage", + "gencode". Default is variant. :param data_type: Data type (exomes, genomes, or joint). Default is session value. :param version: gnomAD version. Default is session value. :return: Hail Table for requested dataset, data type, and version. @@ -129,40 +189,62 @@ def _get_gnomad_release( if ht is not None: return ht - # Use session defaults if parameters are not provided. - data_type = data_type or gnomad_session.data_type - version = version or gnomad_session.version - - # Get all releases for the given dataset. - releases = RELEASES.get(dataset) - # Validate dataset. - if releases is None: - raise ValueError(f"{dataset} is invalid. Choose from {list(RELEASES.keys())}") + dataset_info = SUPPORTED_DATASETS.get(dataset) or SUPPORTED_REFERENCE_DATA.get( + dataset + ) - # Get all releases for the given dataset and data_type. - data_type_releases = releases.get(data_type) + if dataset_info is None: + raise ValueError( + f"{dataset} is invalid. Choose from:\n" + f"\tgnomAD datasets:{list(SUPPORTED_DATASETS.keys())}\n" + f"\treference datasets: {list(SUPPORTED_REFERENCE_DATA.keys())}" + ) - # Validate data type. - if data_type_releases is None: - raise ValueError(f"Invalid data_type '{data_type}' for dataset '{dataset}'.") + # If version is not provided, use the session information. + if dataset == "variant": + version = version or gnomad_session.version + else: + version = version or gnomad_session.compatible_datasets[dataset] - # Check version availability for GRCh38 and GRCh37. - if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]: - return ( - getattr(grch38_gnomad, DATASETS[dataset])(data_type).versions[version].ht() + # Validate version. + versions = dataset_info["versions"] + version_info = versions.get(version) + if version_info is None: + version_format = ", ".join( + f"{v} ({versions[v]['reference_genome']})" for v in versions ) - elif data_type_releases["GRCh37"] and version in data_type_releases["GRCh37"]: - return ( - getattr(grch37_gnomad, DATASETS[dataset])(data_type).versions[version].ht() - ) - else: raise ValueError( - f"Version {version} is not available for {data_type} in the {dataset} dataset. " - f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, " - f"GRCh37 - {data_type_releases['GRCh37']}." + f"Version {version} is not in the supported versions for {dataset}. " + f"Supported versions: {version_format}" ) + # Validate data type. + data_types = version_info.get("data_types") + if data_types: + data_type = data_type or gnomad_session.data_type + if data_type and data_type not in data_types: + raise ValueError( + f"Version {version} is not available for {data_type} in the {dataset} " + f"dataset. Available data types: {data_types}." + ) + + # Get the resource for the given build. + build = version_info["reference_genome"] + res_build = RESOURCES_BY_BUILD[build] + res_name = dataset_info["resource"] + res = getattr(res_build.gnomad, res_name, None) or getattr( + res_build.reference_data, res_name + ) + + if isinstance(res, Callable): + res = res(data_type) if data_type else res() + + if isinstance(res, VersionedTableResource): + res = res.versions[version] + + return res.ht() + def get_gnomad_release( dataset: str = "variant", @@ -170,33 +252,87 @@ def get_gnomad_release( version: Optional[str] = None, ) -> hl.Table: """ - Get gnomAD HT by dataset, data type, and version. + Get gnomAD HT by dataset, data type, and version. + + Not all combinations of dataset, data type, and version are available and/or + supported by the toolbox. The table below shows what is supported. - .. table:: Available versions for each dataset and data type are (as of 2024-10-29) + .. table:: Available versions for each dataset and data type are (as of 2025-1-13) :widths: auto - +--------------+-----------------+----------------------------------+----------------------+ - | Dataset | Data Type | GRCh38 Versions | GRCh37 Versions | - +==============+=================+==================================+======================+ - | variant | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | +-----------------+----------------------------------+----------------------+ - | | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | +-----------------+----------------------------------+----------------------+ - | | joint | 4.1 | N/A | - +--------------+-----------------+----------------------------------+----------------------+ - | coverage | exomes | 4.0 | 2.1 | - | +-----------------+----------------------------------+----------------------+ - | | genomes | 3.0.1 | 2.1 | - +--------------+-----------------+----------------------------------+----------------------+ - | all_sites_an | exomes | 4.1 | N/A | - | +-----------------+----------------------------------+----------------------+ - | | genomes | 4.1 | N/A | - +--------------+-----------------+----------------------------------+----------------------+ - - :param data_type: Data type (exomes, genomes, or joint). Default is "exomes". - :param version: gnomAD version. Default is the current exome release. - :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default - is "variant". + +--------------+--------------+---------+------------------------------+ + | Genome Build | Dataset | Version | Data Types | + +==============+==============+=========+==============================+ + | GRCh37 | variant | 2.1.1 | exomes, genomes | + | +--------------+---------+------------------------------+ + | | coverage | 2.1 | exomes, genomes | + | +--------------+---------+------------------------------+ + | | constraint | 2.1.1 | N/A | + | +--------------+---------+------------------------------+ + | + pext | v7 | base_level, annotation_level | + | +--------------+---------+------------------------------+ + | | liftover | 2.1.1 | exomes, genomes | + +--------------+--------------+---------+------------------------------+ + | GRCh38 | variant | 4.1 | exomes, genomes, joint | + | +--------------+---------+------------------------------+ + | | all_sites_an | 4.1 | exomes, genomes | + | +--------------+---------+------------------------------+ + | | browser | 4.1 | N/A (joint, but doesn't need | + | | | | to be specified) | + | +--------------+---------+------------------------------+ + | | coverage | 3.0.1 | genomes | + | +--------------+---------+------------------------------+ + | | constraint | 4.1 | N/A | + | +--------------+---------+------------------------------+ + | | pext | v10 | base_level, annotation_level | + +--------------+--------------+---------+------------------------------+ + + :param dataset: Dataset type. One of "variant", "coverage", "all_sites_an", + "constraint", "liftover", "pext", "browser". Default is "variant". + :param data_type: Data type. One of "exomes", "genomes", "joint" for all datasets + except "pext" where it is one of "base_level", "annotation_level". Default is + the current session data type. + :param version: gnomAD dataset version. Default is the current session version. :return: Hail Table for requested dataset, data type, and version. """ - return _get_gnomad_release(dataset=dataset, data_type=data_type, version=version) + return _get_dataset(dataset=dataset, data_type=data_type, version=version) + + +def get_compatible_dataset_versions( + dataset: str, variant_version: Optional[str] = None, data_type: Optional[str] = None +) -> Union[str, dict]: + """ + Get the compatible version of another datasets for a given gnomAD variant data version. + + :param dataset: Dataset to get the compatible version for. + :param variant_version: Optional gnomAD variant data version. If not provided, the + current session version is used. + :param data_type: Optional data type for the dataset if applicable. + :return: Compatible version of the dataset for the given variant version. + """ + # Get the dictionary of compatible versions for the given variant version or + # the current session version. + if variant_version is None: + versions = gnomad_session.compatible_datasets + else: + versions = VARIANT_DATA[variant_version]["dataset_versions"] + + # Validate dataset. + if dataset not in versions: + raise ValueError( + f"{dataset} is not available for {variant_version}." + f"Available datasets: {list(versions.keys())}" + ) + + # If the dataset has multiple data types and a data type is provided, return the + # version for the data type. + dataset_version = versions[dataset] + if data_type and isinstance(dataset_version, dict): + if data_type not in dataset_version: + raise ValueError( + f"{data_type} is not available for {variant_version} {dataset}." + f"Available data types: {list(dataset_version.keys())}" + ) + return dataset_version[data_type] + + return dataset_version diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb index 1801596..5454f80 100644 --- a/gnomad_toolbox/notebooks/explore_release_data.ipynb +++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb @@ -51,15 +51,28 @@ }, "source": [ "

Table of Contents

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" 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" + } + }, "cell_type": "markdown", "id": "8e713032", "metadata": {}, "source": [ - "## Import modules" + "## Import the loading function `get_gnomad_release`\n", + "\n", + "The main toolbox function that loads gnomAD release data is `get_gnomad_release`.\n", + "\n", + "![Screenshot 2025-01-14 at 11.02.35 AM.png](attachment:8fbdba40-9387-4520-a2d0-370aaeed6f00.png)\n", + "![Screenshot 2025-01-14 at 11.07.19 AM.png](attachment:53765947-f8a0-4821-aed7-99c4b6de5c25.png)" ] }, { @@ -86,7 +99,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -258,7 +271,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n", + " const el = document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -364,7 +377,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -380,7 +393,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -406,7 +419,7 @@ " / /_/ /__ __/ /\n", " / __ / _ `/ / /\n", " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", - "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-1438-0.2.132-678e1f52b999.log\n" + "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1114-0.2.132-678e1f52b999.log\n" ] } ], @@ -414,6 +427,103 @@ "hl.init(backend=\"local\")" ] }, + { + "cell_type": "markdown", + "id": "3347caa3-1656-4cec-87a3-727a30185f13", + "metadata": {}, + "source": [ + "## Table of all currently supported gnomAD release datasets\n", + "\n", + "The following are the datasets, reference genome build, version, and data type combinations are currently supported for loading in the gnomAD toolbox with the `get_gnomad_release` function.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4b633dd-9b92-4e3f-b6c7-1100fa813885", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Genome BuildDatasetdatasetversiondata_type
GRCh37Variant datavariant2.1.1exomes, genomes
Coveragecoverage2.1exomes, genomes
Gene constraintconstraint2.1.1N/A
Proportion expressed
across transcripts score
pextv7base_level, annotation_level
Lifted over variant data
(GRCh37 --> GRCh38)
liftover2.1.1exomes, genomes
GRCh38Variant datavariant4.1exomes, genomes, joint
All sites allele numbersall_sites_an4.1exomes, genomes
Browser variant databrowser4.1N/A
Note: This includes information for
the joint, exomes, and genomes,
but doesn't need to be specified
Coveragecoverage3.0.1genomes
4.0exomes
Gene constraintconstraint4.1N/A
Proportion expressed
across transcripts score
pextv10base_level, annotation_level
\n" + ] + }, { "cell_type": "markdown", "id": "5335a135", @@ -423,14 +533,6 @@ "source": [ "## Variant data\n", "\n", - "Available versions for each data type and reference build are (as of 2024-10-29):\n", - "\n", - "| Data Type | GRCh38 Versions | GRCh37 Versions |\n", - "|-----------------|----------------------------------|----------------------|\n", - "| exomes | 4.0, 4.1 | 2.1, 2.1.1 |\n", - "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", - "| joint | 4.1 | N/A |\n", - "\n", "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page." ] }, @@ -441,7 +543,9 @@ "tags": [] }, "source": [ - "### v4.1 exomes Hail Table" + "### Exomes Hail Table\n", + "\n", + "Showing gnomAD **v4.1** exomes frequency. " ] }, { @@ -1941,7 +2045,9 @@ "tags": [] }, "source": [ - "### v4.1 genomes Hail Table" + "### Genomes Hail Table\n", + "\n", + "Showing gnomAD **v4.1** genomes frequency. " ] }, { @@ -3312,7 +3418,7 @@ "tags": [] }, "source": [ - "### v4.1 Joint Frequency Hail Table" + "### Joint Frequency Hail Table" ] }, { @@ -3320,7 +3426,7 @@ "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2", "metadata": {}, "source": [ - "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page." + "Showing gnomAD **v4.1** joint frequency. The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page." ] }, { @@ -5916,7 +6022,9 @@ "tags": [] }, "source": [ - "### Exomes all sites allele number Hail Table" + "### Exomes all sites allele number Hail Table\n", + "\n", + "Showing gnomAD **v4.1** exomes all sites allele numbers." ] }, { @@ -6087,7 +6195,9 @@ "tags": [] }, "source": [ - "### Genomes all sites allele number Hail Table" + "### Genomes all sites allele number Hail Table\n", + "\n", + "Showing gnomAD **v4.1** genomes all sites allele numbers." ] }, { @@ -6226,36 +6336,30 @@ }, { "cell_type": "markdown", - "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8", - "metadata": { - "tags": [] - }, - "source": [ - "## Coverage\n" - ] - }, - { - "cell_type": "markdown", - "id": "de70c319-787b-4d6c-9058-255a1137d81f", + "id": "ae3e5ba8-075a-455c-8e5c-e244970d5217", "metadata": { "tags": [] }, "source": [ - "### Exomes coverage Hail Table" + "## Browser variant data\n", + "\n", + "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/news/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page. \n", + "\n", + "Showing the gnomAD **v4.1** browser variant Hail Table." ] }, { "cell_type": "markdown", - "id": "3278430c-4279-4d89-85e7-276184ec42b8", + "id": "d7c13865-814a-46e8-a4f3-c823a910cfa3", "metadata": {}, "source": [ - "#### Load the Hail Table\n" + "### Load the Hail Table\n" ] }, { "cell_type": "code", "execution_count": 18, - "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2", + "id": "329f08ef-6dbf-42cd-bbbc-d3f5396708ac", "metadata": { "ExecuteTime": { "end_time": "2024-12-06T20:08:59.197890Z", @@ -6264,175 +6368,21 @@ }, "outputs": [], "source": [ - "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")" + "ht = get_gnomad_release(version='4.1', dataset=\"browser\")" ] }, { "cell_type": "markdown", - "id": "128e58ce-c219-472a-88be-6babc2ba5a15", + "id": "6ec344e0-4951-44d8-8f7b-1283a427914d", "metadata": {}, "source": [ - "#### Print the schema of the Hail Table\n" + "### Print the schema of the Hail Table\n" ] }, { "cell_type": "code", "execution_count": 19, - "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-06T20:09:00.032524Z", - "start_time": "2024-12-06T20:09:00.029271Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------------------------------------\n", - "Global fields:\n", - " None\n", - "----------------------------------------\n", - "Row fields:\n", - " 'locus': locus \n", - " 'mean': float64 \n", - " 'median_approx': int32 \n", - " 'total_DP': int64 \n", - " 'over_1': float64 \n", - " 'over_5': float64 \n", - " 'over_10': float64 \n", - " 'over_15': float64 \n", - " 'over_20': float64 \n", - " 'over_25': float64 \n", - " 'over_30': float64 \n", - " 'over_50': float64 \n", - " 'over_100': float64 \n", - "----------------------------------------\n", - "Key: ['locus']\n", - "----------------------------------------\n" - ] - } - ], - "source": [ - "ht.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "5969ab0c-7cee-4061-8740-8b82366ae806", - "metadata": { - "tags": [] - }, - "source": [ - "#### Show the first 5 variants in the Hail Table\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-06T20:09:20.949958Z", - "start_time": "2024-12-06T20:09:05.622171Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

showing top 5 rows

\n" - ], - "text/plain": [ - "+---------------+----------+---------------+----------+----------+----------+\n", - "| locus | mean | median_approx | total_DP | over_1 | over_5 |\n", - "+---------------+----------+---------------+----------+----------+----------+\n", - "| locus | float64 | int32 | int64 | float64 | float64 |\n", - "+---------------+----------+---------------+----------+----------+----------+\n", - "| chr1:11819 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", - "| chr1:11820 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", - "| chr1:11821 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", - "| chr1:11822 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", - "| chr1:11823 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", - "+---------------+----------+---------------+----------+----------+----------+\n", - "\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| over_10 | over_15 | over_20 | over_25 | over_30 | over_50 | over_100 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| float64 | float64 | float64 | float64 | float64 | float64 | float64 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", - "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", - "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", - "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", - "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "showing top 5 rows" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ht.show(5)" - ] - }, - { - "cell_type": "markdown", - "id": "19b2e9be-48fd-4859-af3d-a0775656d24c", - "metadata": { - "tags": [] - }, - "source": [ - "### Genomes coverage Hail Table" - ] - }, - { - "cell_type": "markdown", - "id": "5918f8e0-a723-439a-a11f-558d5ed13be8", - "metadata": {}, - "source": [ - "#### Load the Hail Table\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a8d0be07-c35d-425a-b554-c86034e367fc", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-06T20:08:59.197890Z", - "start_time": "2024-12-06T20:08:50.859108Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")" - ] - }, - { - "cell_type": "markdown", - "id": "d129b898-d642-44d1-8243-66a9cca8d1b1", - "metadata": {}, - "source": [ - "#### Print the schema of the Hail Table\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76", + "id": "17efacb6-84c4-4345-9bf9-1dca259dd7fa", "metadata": { "ExecuteTime": { "end_time": "2024-12-06T20:09:00.032524Z", @@ -6447,113 +6397,8227 @@ "text": [ "----------------------------------------\n", "Global fields:\n", - " None\n", + " 'mane_select_version': str \n", "----------------------------------------\n", "Row fields:\n", " 'locus': locus \n", - " 'mean': float64 \n", - " 'median': int32 \n", - " 'count_array': array \n", - " 'over_1': float32 \n", - " 'over_5': float32 \n", - " 'over_10': float32 \n", - " 'over_15': float32 \n", - " 'over_20': float32 \n", - " 'over_25': float32 \n", - " 'over_30': float32 \n", - " 'over_50': float32 \n", - " 'over_100': float32 \n", - "----------------------------------------\n", - "Key: ['locus']\n", - "----------------------------------------\n" - ] - } - ], - "source": [ - "ht.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82", - "metadata": { - "tags": [] - }, - "source": [ - "#### Show the first 5 variants in the Hail Table\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b27cb655-3abb-4501-bcc9-3f634db64591", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-06T20:09:20.949958Z", - "start_time": "2024-12-06T20:09:05.622171Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
locus
mean
median
count_array
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04

showing top 5 rows

\n" - ], - "text/plain": [ - "+---------------+----------+--------+\n", - "| locus | mean | median |\n", - "+---------------+----------+--------+\n", - "| locus | float64 | int32 |\n", - "+---------------+----------+--------+\n", - "| chr1:10001 | 1.93e+01 | 16 |\n", - "| chr1:10002 | 2.10e+01 | 18 |\n", - "| chr1:10003 | 2.44e+01 | 23 |\n", - "| chr1:10004 | 2.43e+01 | 23 |\n", - "| chr1:10005 | 2.45e+01 | 23 |\n", - "+---------------+----------+--------+\n", - "\n", - "+------------------------------------------------------------------------------+\n", - "| count_array |\n", - "+------------------------------------------------------------------------------+\n", - "| array |\n", - "+------------------------------------------------------------------------------+\n", - "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n", - "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n", - "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n", - "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n", - "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n", - "+------------------------------------------------------------------------------+\n", - "\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| over_1 | over_5 | over_10 | over_15 | over_20 | over_25 | over_30 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| float32 | float32 | float32 | float32 | float32 | float32 | float32 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", - "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n", - "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n", - "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n", - "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n", - "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n", - "+----------+----------+----------+----------+----------+----------+----------+\n", + " 'alleles': array \n", + " 'exome': struct {\n", + " colocated_variants: struct {\n", + " all: array, \n", + " non_ukb: array\n", + " }, \n", + " subsets: set, \n", + " flags: set, \n", + " freq: struct {\n", + " all: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int64, \n", + " ancestry_groups: array\n", + " }, \n", + " non_ukb: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int64, \n", + " ancestry_groups: array\n", + " }\n", + " }, \n", + " fafmax: struct {\n", + " gnomad: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " non_ukb: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }\n", + " }, \n", + " age_distribution: struct {\n", + " het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " filters: set, \n", + " quality_metrics: struct {\n", + " allele_balance: struct {\n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " genotype_depth: struct {\n", + " all_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " all_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " genotype_quality: struct {\n", + " all_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " all_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " site_quality_metrics: array\n", + " }\n", + " } \n", + " 'genome': struct {\n", + " colocated_variants: struct {\n", + " hgdp: array, \n", + " tgp: array, \n", + " all: array\n", + " }, \n", + " subsets: set, \n", + " flags: set, \n", + " freq: struct {\n", + " hgdp: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int32, \n", + " ancestry_groups: array\n", + " }, \n", + " tgp: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int32, \n", + " ancestry_groups: array\n", + " }, \n", + " all: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int32, \n", + " ancestry_groups: array\n", + " }\n", + " }, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " age_distribution: struct {\n", + " het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " filters: set, \n", + " quality_metrics: struct {\n", + " allele_balance: struct {\n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " genotype_depth: struct {\n", + " all_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " all_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " genotype_quality: struct {\n", + " all_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " all_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_adj: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " alt_raw: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " site_quality_metrics: array\n", + " }\n", + " } \n", + " 'rsids': set \n", + " 'in_silico_predictors': struct {\n", + " cadd: struct {\n", + " phred: float32, \n", + " raw_score: float32\n", + " }, \n", + " revel_max: float64, \n", + " spliceai_ds_max: float32, \n", + " pangolin_largest_ds: float64, \n", + " phylop: float64, \n", + " sift_max: float64, \n", + " polyphen_max: float64\n", + " } \n", + " 'variant_id': str \n", + " 'colocated_variants': struct {\n", + " all: array, \n", + " non_ukb: array, \n", + " hgdp: array, \n", + " tgp: array\n", + " } \n", + " 'joint': struct {\n", + " freq: struct {\n", + " all: struct {\n", + " ac: int32, \n", + " ac_raw: int32, \n", + " an: int32, \n", + " hemizygote_count: int32, \n", + " homozygote_count: int32, \n", + " ancestry_groups: array\n", + " }\n", + " }, \n", + " faf: array, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }, \n", + " flags: set, \n", + " freq_comparison_stats: struct {\n", + " contingency_table_test: array, \n", + " cochran_mantel_haenszel_test: struct {\n", + " p_value: float64, \n", + " chisq: float64\n", + " }, \n", + " stat_union: struct {\n", + " p_value: float64, \n", + " stat_test_name: str, \n", + " gen_ancs: array\n", + " }\n", + " }\n", + " } \n", + " 'coverage': struct {\n", + " exome: struct {\n", + " mean: float64, \n", + " median_approx: int32, \n", + " total_DP: int64, \n", + " over_1: float64, \n", + " over_5: float64, \n", + " over_10: float64, \n", + " over_15: float64, \n", + " over_20: float64, \n", + " over_25: float64, \n", + " over_30: float64, \n", + " over_50: float64, \n", + " over_100: float64\n", + " }, \n", + " genome: struct {\n", + " mean: float64, \n", + " median_approx: int32, \n", + " total_DP: int64, \n", + " over_1: float32, \n", + " over_5: float32, \n", + " over_10: float32, \n", + " over_15: float32, \n", + " over_20: float32, \n", + " over_25: float32, \n", + " over_30: float32, \n", + " over_50: float32, \n", + " over_100: float32\n", + " }\n", + " } \n", + " 'transcript_consequences': array, \n", + " domains: set, \n", + " gene_id: str, \n", + " gene_symbol: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " is_canonical: bool, \n", + " lof_filter: str, \n", + " lof_flags: str, \n", + " lof: str, \n", + " major_consequence: str, \n", + " transcript_id: str, \n", + " transcript_version: str, \n", + " gene_version: str, \n", + " is_mane_select: bool, \n", + " is_mane_select_version: bool, \n", + " refseq_id: str, \n", + " refseq_version: str\n", + " }> \n", + " 'caid': str \n", + " 'vrs': struct {\n", + " ref: struct {\n", + " allele_id: str, \n", + " start: int32, \n", + " end: int32, \n", + " state: str\n", + " }, \n", + " alt: struct {\n", + " allele_id: str, \n", + " start: int32, \n", + " end: int32, \n", + " state: str\n", + " }\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "e113c4a2-25c2-4f55-bbb9-382c87cd770c", + "metadata": { + "tags": [] + }, + "source": [ + "### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d03dd50b-37ac-49ec-b766-a86870ee6573", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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state
locus<GRCh38>array<str>array<str>array<str>set<str>set<str>int32int32int32int32int64array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int64}>int32int32int32int32int64array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int64}>float64strfloat64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{metric: str, value: float64}>array<str>array<str>array<str>set<str>set<str>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>float64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{metric: str, value: float64}>set<str>float32float32float64float32float64float64float64float64strarray<str>array<str>array<str>array<str>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>float64int32int64float64float64float64float64float64float64float64float64float64float64int32int64float32float32float32float32float32float32float32float32float32array<struct{biotype: str, consequence_terms: array<str>, domains: set<str>, gene_id: str, gene_symbol: str, hgvsc: str, hgvsp: str, is_canonical: bool, lof_filter: str, lof_flags: str, lof: str, major_consequence: str, transcript_id: str, transcript_version: str, gene_version: str, is_mane_select: bool, is_mane_select_version: bool, refseq_id: str, refseq_version: str}>strstrint32int32strstrint32int32str
chr1:10031["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}0081200[]00107800[]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AC0","AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[("SiteQuality",9.60e+01),("inbreeding_coeff",-1.65e-05),("AS_MQ",3.51e+01),("AS_FS",5.10e+00),("AS_MQRankSum",-5.72e-01),("AS_pab_max",6.87e-01),("AS_QUALapprox",7.70e+01),("AS_QD",2.96e+00),("AS_ReadPosRankSum",-1.38e+00),("AS_SOR",9.64e-02),("AS_VarDP",2.60e+01),("AS_VQSLOD",-4.57e+00)]{"rs1639542312"}8.97e+007.57e-01NANANANANANA"1-10031-T-C"[][][][]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("",0,56642,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3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chr1:10055["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{"hgdp"}{"lcr","segdup"}01131200[("japanese",0,50,0,0),("japanese_XX",0,12,0,0),("japanese_XY",0,38,0,0),("adygei",0,22,0,0),("adygei_XX",0,16,0,0),("adygei_XY",0,6,0,0),("orcadian",0,26,0,0),("orcadian_XX",0,14,0,0),("orcadian_XY",0,12,0,0),("bantusouthafrica",0,8,0,0),("bantusouthafrica_XX",0,0,0,0),("bantusouthafrica_XY",0,8,0,0),("yakut",0,36,0,0),("yakut_XX",0,14,0,0),("yakut_XY",0,22,0,0),("han",0,58,0,0),("han_XX",0,32,0,0),("han_XY",0,26,0,0),("uygur",0,10,0,0),("uygur_XX",0,4,0,0),("uygur_XY",0,6,0,0),("balochi",0,40,0,0),("balochi_XX",0,0,0,0),("balochi_XY",0,40,0,0),("bedouin",0,72,0,0),("bedouin_XX",0,28,0,0),("bedouin_XY",0,44,0,0),("russian",0,48,0,0),("russian_XX",0,18,0,0),("russian_XY",0,30,0,0),("daur",0,10,0,0),("daur_XX",0,2,0,0),("daur_XY",0,8,0,0),("pima",0,16,0,0),("pima_XX",0,6,0,0),("pima_XY",0,10,0,0),("hezhen",0,14,0,0),("hezhen_XX",0,4,0,0),("hezhen_XY",0,10,0,0),("biaka",0,20,0,0),("biaka_XX",0,0,0,0),("biaka_XY",0,20,0,0),("miao",0,8,0,0),("miao_XX",0,2,0,0),("miao_XY",0,6,0,0),("sindhi",0,40,0,0),("sindhi_XX",0,8,0,0),("sindhi_XY",0,32,0,0),("northernhan",0,14,0,0),("northernhan_XX",0,2,0,0),("northernhan_XY",0,12,0,0),("oroqen",0,14,0,0),("oroqen_XX",0,6,0,0),("oroqen_XY",0,8,0,0),("san",0,12,0,0),("san_XX",0,0,0,0),("san_XY",0,12,0,0),("tu",0,18,0,0),("tu_XX",0,4,0,0),("tu_XY",0,14,0,0),("tuscan",0,12,0,0),("tuscan_XX",0,4,0,0),("tuscan_XY",0,8,0,0),("mbuti",0,16,0,0),("mbuti_XX",0,4,0,0),("mbuti_XY",0,12,0,0),("palestinian",0,58,0,0),("palestinian_XX",0,38,0,0),("palestinian_XY",0,20,0,0),("tujia",0,16,0,0),("tujia_XX",0,2,0,0),("tujia_XY",0,14,0,0),("druze",0,58,0,0),("druze_XX",0,42,0,0),("druze_XY",0,16,0,0),("pathan",0,34,0,0),("pathan_XX",0,4,0,0),("pathan_XY",0,30,0,0),("basque",0,38,0,0),("basque_XX",0,12,0,0),("basque_XY",0,26,0,0),("makrani",0,38,0,0),("makrani_XX",0,6,0,0),("makrani_XY",0,32,0,0),("burusho",0,40,0,0),("burusho_XX",0,6,0,0),("burusho_XY",0,34,0,0),("mongolian",0,10,0,0),("mongolian_XX",0,4,0,0),("mongolian_XY",0,6,0,0),("bougainville",0,10,0,0),("bougainville_XX",0,10,0,0),("bougainville_XY",0,0,0,0),("papuansepik",0,2,0,0),("papuansepik_XX",0,2,0,0),("papuansepik_XY",0,0,0,0),("yi",0,18,0,0),("yi_XX",0,2,0,0),("yi_XY",0,16,0,0),("naxi",0,8,0,0),("naxi_XX",0,2,0,0),("naxi_XY",0,6,0,0),("lahu",0,8,0,0),("lahu_XX",0,0,0,0),("lahu_XY",0,8,0,0),("sardinian",0,52,0,0),("sardinian_XX",0,24,0,0),("sardinian_XY",0,28,0,0),("karitiana",0,14,0,0),("karitiana_XX",0,12,0,0),("karitiana_XY",0,2,0,0),("mozabite",0,36,0,0),("mozabite_XX",0,12,0,0),("mozabite_XY",0,24,0,0),("yoruba",0,20,0,0),("yoruba_XX",0,10,0,0),("yoruba_XY",0,10,0,0),("dai",0,12,0,0),("dai_XX",0,6,0,0),("dai_XY",0,6,0,0),("bergamoitalian",0,14,0,0),("bergamoitalian_XX",0,8,0,0),("bergamoitalian_XY",0,6,0,0),("cambodian",0,14,0,0),("cambodian_XX",0,8,0,0),("cambodian_XY",0,6,0,0),("french",0,48,0,0),("french_XX",0,26,0,0),("french_XY",0,22,0,0),("mandenka",0,26,0,0),("mandenka_XX",0,10,0,0),("mandenka_XY",0,16,0,0),("surui",0,12,0,0),("surui_XX",0,8,0,0),("surui_XY",0,4,0,0),("brahui",0,30,0,0),("brahui_XX",0,0,0,0),("brahui_XY",0,30,0,0),("hazara",0,22,0,0),("hazara_XX",0,0,0,0),("hazara_XY",0,22,0,0),("kalash",0,28,0,0),("kalash_XX",0,6,0,0),("kalash_XY",0,22,0,0),("papuanhighlands",0,4,0,0),("papuanhighlands_XX",0,2,0,0),("papuanhighlands_XY",0,2,0,0),("xibo",0,10,0,0),("xibo_XX",0,0,0,0),("xibo_XY",0,10,0,0),("colombian",0,6,0,0),("colombian_XX",0,4,0,0),("colombian_XY",0,2,0,0),("bantukenya",0,10,0,0),("bantukenya_XX",0,2,0,0),("bantukenya_XY",0,8,0,0),("she",0,18,0,0),("she_XX",0,6,0,0),("she_XY",0,12,0,0),("maya",0,34,0,0),("maya_XX",0,30,0,0),("maya_XY",0,4,0,0),("XX",0,484,0,0),("XY",0,828,0,0)]00147800[]159422400[("remaining",0,1228,0,0),("remaining_XX",0,624,0,0),("remaining_XY",0,604,0,0),("amr",0,8056,0,0),("amr_XX",0,3902,0,0),("amr_XY",0,4154,0,0),("fin",1,5570,0,0),("fin_XX",1,1034,0,0),("fin_XY",0,4536,0,0),("ami",0,682,0,0),("ami_XX",0,344,0,0),("ami_XY",0,338,0,0),("eas",0,3020,0,0),("eas_XX",0,1256,0,0),("eas_XY",0,1764,0,0),("mid",0,216,0,0),("mid_XX",0,110,0,0),("mid_XY",0,106,0,0),("sas",0,2668,0,0),("sas_XX",0,600,0,0),("sas_XY",0,2068,0,0),("asj",0,2370,0,0),("asj_XX",0,1246,0,0),("asj_XY",0,1124,0,0),("afr",0,26032,0,0),("afr_XX",0,14152,0,0),("afr_XY",0,11880,0,0),("nfe",0,44382,0,0),("nfe_XX",0,26312,0,0),("nfe_XY",0,18070,0,0),("XX",1,49580,0,0),("XY",0,44644,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.5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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+------------------------------+\n", + "| locus | alleles | exome.colocated_variants.all |\n", + "+---------------+------------+------------------------------+\n", + "| locus | array | array |\n", + "+---------------+------------+------------------------------+\n", + "| chr1:10031 | [\"T\",\"C\"] | NA |\n", + "| chr1:10037 | [\"T\",\"C\"] | NA |\n", + "| chr1:10043 | [\"T\",\"C\"] | NA |\n", + "| chr1:10055 | [\"T\",\"C\"] | NA |\n", + "| chr1:10057 | [\"A\",\"C\"] | NA |\n", + "+---------------+------------+------------------------------+\n", + "\n", + "+----------------------------------+---------------+-------------+\n", + "| exome.colocated_variants.non_ukb | exome.subsets | exome.flags |\n", + "+----------------------------------+---------------+-------------+\n", + "| array | set | set |\n", + "+----------------------------------+---------------+-------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | 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"+------------------------+------------------------+-------------------------+\n", + "\n", + "+----------------------+-------------------------------+\n", + "| coverage.genome.mean | coverage.genome.median_approx |\n", + "+----------------------+-------------------------------+\n", + "| float64 | int32 |\n", + "+----------------------+-------------------------------+\n", + "| 3.74e+01 | 38 |\n", + "| 4.29e+01 | 44 |\n", + "| 4.42e+01 | 47 |\n", + "| 4.54e+01 | 51 |\n", + "| 5.43e+01 | 58 |\n", + "+----------------------+-------------------------------+\n", + "\n", + "+--------------------------+------------------------+------------------------+\n", + "| coverage.genome.total_DP | coverage.genome.over_1 | coverage.genome.over_5 |\n", + "+--------------------------+------------------------+------------------------+\n", + "| int64 | float32 | float32 |\n", + "+--------------------------+------------------------+------------------------+\n", + "| 2684436 | 8.08e-01 | 8.08e-01 |\n", + "| 3077955 | 8.43e-01 | 8.43e-01 |\n", + "| 3165823 | 8.06e-01 | 8.06e-01 |\n", + "| 3252282 | 7.16e-01 | 7.16e-01 |\n", + "| 3891700 | 8.20e-01 | 8.20e-01 |\n", + "+--------------------------+------------------------+------------------------+\n", + "\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| coverage.genome.over_10 | coverage.genome.over_15 | coverage.genome.over_20 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| float32 | float32 | float32 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| 8.07e-01 | 8.01e-01 | 7.82e-01 |\n", + "| 8.42e-01 | 8.39e-01 | 8.27e-01 |\n", + "| 8.05e-01 | 8.03e-01 | 7.97e-01 |\n", + "| 7.15e-01 | 7.15e-01 | 7.13e-01 |\n", + "| 8.20e-01 | 8.20e-01 | 8.18e-01 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| coverage.genome.over_25 | coverage.genome.over_30 | coverage.genome.over_50 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| float32 | float32 | float32 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "| 7.43e-01 | 6.67e-01 | 2.89e-01 |\n", + "| 8.02e-01 | 7.48e-01 | 3.93e-01 |\n", + "| 7.82e-01 | 7.48e-01 | 4.50e-01 |\n", + "| 7.10e-01 | 7.00e-01 | 5.30e-01 |\n", + "| 8.15e-01 | 8.07e-01 | 6.45e-01 |\n", + "+-------------------------+-------------------------+-------------------------+\n", + "\n", + "+--------------------------+\n", + "| coverage.genome.over_100 |\n", + "+--------------------------+\n", + "| float32 |\n", + "+--------------------------+\n", + "| 1.13e-02 |\n", + "| 1.66e-02 |\n", + "| 2.14e-02 |\n", + "| 3.76e-02 |\n", + "| 5.38e-02 |\n", + "+--------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| transcript_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, domains: set \n", + " 'mean': float64 \n", + " 'median_approx': int32 \n", + " 'total_DP': int64 \n", + " 'over_1': float64 \n", + " 'over_5': float64 \n", + " 'over_10': float64 \n", + " 'over_15': float64 \n", + " 'over_20': float64 \n", + " 'over_25': float64 \n", + " 'over_30': float64 \n", + " 'over_50': float64 \n", + " 'over_100': float64 \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "5969ab0c-7cee-4061-8740-8b82366ae806", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+----------+---------------+----------+----------+----------+\n", + "| locus | mean | median_approx | total_DP | over_1 | over_5 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "| locus | float64 | int32 | int64 | float64 | float64 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "| chr1:11819 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", + "| chr1:11820 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", + "| chr1:11821 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", + "| chr1:11822 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", + "| chr1:11823 | 1.12e-01 | 0 | 81695 | 2.79e-02 | 7.59e-03 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| over_10 | over_15 | over_20 | over_25 | over_30 | over_50 | over_100 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| float64 | float64 | float64 | float64 | float64 | float64 | float64 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "19b2e9be-48fd-4859-af3d-a0775656d24c", + "metadata": { + "tags": [] + }, + "source": [ + "### Genomes coverage Hail Table\n", + "\n", + "Showing gnomAD **v3.0.1** coverage." + ] + }, + { + "cell_type": "markdown", + "id": "5918f8e0-a723-439a-a11f-558d5ed13be8", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a8d0be07-c35d-425a-b554-c86034e367fc", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='3.0.1', dataset=\"coverage\")" + ] + }, + { + "cell_type": "markdown", + "id": "d129b898-d642-44d1-8243-66a9cca8d1b1", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " None\n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'mean': float64 \n", + " 'median_approx': int32 \n", + " 'total_DP': int64 \n", + " 'over_1': float32 \n", + " 'over_5': float32 \n", + " 'over_10': float32 \n", + " 'over_15': float32 \n", + " 'over_20': float32 \n", + " 'over_25': float32 \n", + " 'over_30': float32 \n", + " 'over_50': float32 \n", + " 'over_100': float32 \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b27cb655-3abb-4501-bcc9-3f634db64591", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+----------+---------------+----------+----------+----------+\n", + "| locus | mean | median_approx | total_DP | over_1 | over_5 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "| locus | float64 | int32 | int64 | float32 | float32 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "| chr1:10001 | 2.41e+00 | 0 | 172926 | 1.25e-01 | 1.19e-01 |\n", + "| chr1:10002 | 4.61e+00 | 0 | 330760 | 2.20e-01 | 2.15e-01 |\n", + "| chr1:10003 | 6.38e+00 | 0 | 457611 | 2.62e-01 | 2.59e-01 |\n", + "| chr1:10004 | 1.04e+01 | 0 | 744840 | 4.27e-01 | 4.24e-01 |\n", + "| chr1:10005 | 1.18e+01 | 0 | 849441 | 4.83e-01 | 4.80e-01 |\n", + "+---------------+----------+---------------+----------+----------+----------+\n", + "\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| over_10 | over_15 | over_20 | over_25 | over_30 | over_50 | over_100 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| float32 | float32 | float32 | float32 | float32 | float32 | float32 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 | 2.27e-03 | 0.00e+00 |\n", + "| 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 | 4.83e-03 | 2.79e-05 |\n", + "| 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 | 7.61e-03 | 5.58e-05 |\n", + "| 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 | 1.20e-02 | 1.67e-04 |\n", + "| 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 | 1.42e-02 | 2.37e-04 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "c4ae4713-f542-46e3-979f-0bcfb44ec56c", + "metadata": { + "tags": [] + }, + "source": [ + "## Gene constraint\n", + "\n", + "For details on gene constraint see the [Help](https://gnomad.broadinstitute.org/help/constraint) and [FAQ](https://gnomad.broadinstitute.org/help#constraint)." + ] + }, + { + "cell_type": "markdown", + "id": "4ed92af6-aacc-4cb2-890d-7ff76a00196d", + "metadata": {}, + "source": [ + "### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3de343dc-37ae-4b17-a056-9d30e71e217c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(version='4.1', dataset=\"constraint\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b59b2f6-aadd-408d-8663-b676d41565bf", + "metadata": {}, + "source": [ + "### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c401dc94-0db0-4902-933d-e46c8aa447ce", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'version': str \n", + " 'apply_model_params': struct {\n", + " max_af: float64, \n", + " pops: tuple (\n", + " ), \n", + " plateau_models: struct {\n", + " total: dict>\n", + " }, \n", + " coverage_model: str\n", + " } \n", + " 'sd_raw_z': struct {\n", + " lof: float64, \n", + " mis: float64, \n", + " syn: float64\n", + " } \n", + "----------------------------------------\n", + "Row fields:\n", + " 'gene': str \n", + " 'gene_id': str \n", + " 'transcript': str \n", + " 'canonical': bool \n", + " 'mane_select': bool \n", + " 'lof_hc_lc': struct {\n", + " obs: int64, \n", + " exp: float64, \n", + " possible: int64, \n", + " oe: float64, \n", + " mu: float64, \n", + " pLI: float64, \n", + " pNull: float64, \n", + " pRec: float64\n", + " } \n", + " 'lof': struct {\n", + " obs: int64, \n", + " exp: float64, \n", + " possible: int64, \n", + " oe: float64, \n", + " mu: float64, \n", + " pLI: float64, \n", + " pNull: float64, \n", + " pRec: float64, \n", + " oe_ci: struct {\n", + " lower: float64, \n", + " upper: float64, \n", + " upper_rank: int64, \n", + " upper_bin_decile: int32\n", + " }, \n", + " z_raw: float64, \n", + " z_score: float64\n", + " } \n", + " 'mis': struct {\n", + " obs: int64, \n", + " exp: float64, \n", + " possible: int64, \n", + " oe: float64, \n", + " mu: float64, \n", + " oe_ci: struct {\n", + " lower: float64, \n", + " upper: float64\n", + " }, \n", + " z_raw: float64, \n", + " z_score: float64\n", + " } \n", + " 'mis_pphen': struct {\n", + " obs: int64, \n", + " exp: float64, \n", + " possible: int64, \n", + " oe: float64\n", + " } \n", + " 'syn': struct {\n", + " obs: int64, \n", + " exp: float64, \n", + " possible: int64, \n", + " oe: float64, \n", + " mu: float64, \n", + " oe_ci: struct {\n", + " lower: float64, \n", + " upper: float64\n", + " }, \n", + " z_raw: float64, \n", + " z_score: float64\n", + " } \n", + " 'constraint_flags': set \n", + " 'level': str \n", + " 'transcript_type': str \n", + " 'chromosome': str \n", + " 'cds_length': int64 \n", + " 'num_coding_exons': int64 \n", + "----------------------------------------\n", + "Key: ['gene', 'gene_id', 'transcript', 'canonical', 'mane_select']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "5b0e64c9-744c-4ed7-a832-9d68c548540b", + "metadata": { + "tags": [] + }, + "source": [ + "### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "262cbbf1-9fc7-4819-9b2a-d1a1e27c10bf", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": 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"A1BG""ENSG00000121410""ENST00000600966"FalseFalse242.63e+011239.14e-014.20e-072.53e-083.43e-016.57e-01222.50e+011198.80e-013.96e-071.84e-072.65e-017.35e-016.28e-011.26e+00NANA6.01e-015.09e-013994.06e+0218589.84e-014.91e-069.06e-011.07e+003.24e-011.18e-011031.10e+025129.40e-011661.79e+026379.28e-011.91e-068.17e-011.06e+009.64e-015.26e-01{}"1""protein_coding""chr19"9175
"A1CF""29974""NM_001198818.2"FalseFalse457.00e+013526.43e-019.55e-077.32e-102.06e-039.98e-01457.00e+013526.43e-019.55e-077.66e-101.96e-039.98e-015.06e-018.25e-01NANA2.98e+002.53e+006537.46e+0237208.76e-014.38e-068.21e-019.34e-013.39e+001.24e+002362.77e+0213998.53e-012722.73e+0211579.98e-012.01e-069.03e-011.10e+003.27e-021.78e-02{}NANANANANA
"A1CF""29974""NM_001198819.2"FalseFalse487.28e+013676.59e-011.02e-067.64e-112.53e-039.97e-01477.03e+013556.69e-019.72e-077.70e-113.58e-039.96e-015.28e-018.53e-01NANA2.78e+002.35e+006707.62e+0238378.80e-014.43e-068.25e-019.38e-013.33e+001.21e+003093.65e+0218418.47e-012712.76e+0211889.82e-011.91e-068.88e-011.09e+003.00e-011.64e-01{}NANANANANA

showing top 5 rows

\n" + ], + "text/plain": [ + "+--------+-------------------+-------------------+-----------+-------------+\n", + "| gene | gene_id | transcript | canonical | mane_select |\n", + "+--------+-------------------+-------------------+-----------+-------------+\n", + "| str | str | str | bool | bool |\n", + "+--------+-------------------+-------------------+-----------+-------------+\n", + "| \"A1BG\" | \"1\" | \"NM_130786.4\" | True | True |\n", + "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000263100\" | True | True |\n", + "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000600966\" | False | False |\n", + "| \"A1CF\" | \"29974\" | \"NM_001198818.2\" | False | False |\n", + "| \"A1CF\" | \"29974\" | \"NM_001198819.2\" | False | False |\n", + "+--------+-------------------+-------------------+-----------+-------------+\n", + "\n", + "+---------------+---------------+--------------------+--------------+\n", + "| lof_hc_lc.obs | lof_hc_lc.exp | lof_hc_lc.possible | lof_hc_lc.oe |\n", + "+---------------+---------------+--------------------+--------------+\n", + "| int64 | float64 | int64 | float64 |\n", + "+---------------+---------------+--------------------+--------------+\n", + "| 45 | 4.30e+01 | 193 | 1.05e+00 |\n", + "| 45 | 4.30e+01 | 193 | 1.05e+00 |\n", + "| 24 | 2.63e+01 | 123 | 9.14e-01 |\n", + "| 45 | 7.00e+01 | 352 | 6.43e-01 |\n", + "| 48 | 7.28e+01 | 367 | 6.59e-01 |\n", + "+---------------+---------------+--------------------+--------------+\n", + "\n", + "+--------------+---------------+-----------------+----------------+---------+\n", + "| lof_hc_lc.mu | lof_hc_lc.pLI | lof_hc_lc.pNull | lof_hc_lc.pRec | lof.obs |\n", + "+--------------+---------------+-----------------+----------------+---------+\n", + "| float64 | float64 | float64 | float64 | int64 |\n", + "+--------------+---------------+-----------------+----------------+---------+\n", + "| 7.06e-07 | 1.63e-16 | 8.49e-01 | 1.51e-01 | 45 |\n", + "| 7.06e-07 | 1.63e-16 | 8.49e-01 | 1.51e-01 | 45 |\n", + "| 4.20e-07 | 2.53e-08 | 3.43e-01 | 6.57e-01 | 22 |\n", + "| 9.55e-07 | 7.32e-10 | 2.06e-03 | 9.98e-01 | 45 |\n", + "| 1.02e-06 | 7.64e-11 | 2.53e-03 | 9.97e-01 | 47 |\n", + "+--------------+---------------+-----------------+----------------+---------+\n", + "\n", + "+----------+--------------+----------+----------+----------+-----------+\n", + "| lof.exp | lof.possible | lof.oe | lof.mu | lof.pLI | lof.pNull |\n", + "+----------+--------------+----------+----------+----------+-----------+\n", + "| float64 | int64 | float64 | float64 | float64 | float64 |\n", + "+----------+--------------+----------+----------+----------+-----------+\n", + "| 4.30e+01 | 193 | 1.05e+00 | 7.06e-07 | 1.77e-16 | 8.43e-01 |\n", + "| 4.30e+01 | 193 | 1.05e+00 | 7.06e-07 | 1.77e-16 | 8.43e-01 |\n", + "| 2.50e+01 | 119 | 8.80e-01 | 3.96e-07 | 1.84e-07 | 2.65e-01 |\n", + "| 7.00e+01 | 352 | 6.43e-01 | 9.55e-07 | 7.66e-10 | 1.96e-03 |\n", + "| 7.03e+01 | 355 | 6.69e-01 | 9.72e-07 | 7.70e-11 | 3.58e-03 |\n", + "+----------+--------------+----------+----------+----------+-----------+\n", + "\n", + "+----------+-----------------+-----------------+----------------------+\n", + "| lof.pRec | lof.oe_ci.lower | lof.oe_ci.upper | lof.oe_ci.upper_rank |\n", + "+----------+-----------------+-----------------+----------------------+\n", + "| float64 | float64 | float64 | int64 |\n", + "+----------+-----------------+-----------------+----------------------+\n", + "| 1.57e-01 | 8.22e-01 | 1.34e+00 | NA |\n", + "| 1.57e-01 | 8.22e-01 | 1.34e+00 | 14057 |\n", + "| 7.35e-01 | 6.28e-01 | 1.26e+00 | NA |\n", + "| 9.98e-01 | 5.06e-01 | 8.25e-01 | NA |\n", + "| 9.96e-01 | 5.28e-01 | 8.53e-01 | NA |\n", + "+----------+-----------------+-----------------+----------------------+\n", + "\n", + "+----------------------------+-----------+-------------+---------+----------+\n", + "| lof.oe_ci.upper_bin_decile | lof.z_raw | lof.z_score | mis.obs | mis.exp |\n", + "+----------------------------+-----------+-------------+---------+----------+\n", + "| int32 | float64 | float64 | int64 | float64 |\n", + "+----------------------------+-----------+-------------+---------+----------+\n", + "| NA | -2.98e-01 | -2.52e-01 | 707 | 6.47e+02 |\n", + "| 7 | -2.98e-01 | -2.52e-01 | 707 | 6.47e+02 |\n", + "| NA | 6.01e-01 | 5.09e-01 | 399 | 4.06e+02 |\n", + "| NA | 2.98e+00 | 2.53e+00 | 653 | 7.46e+02 |\n", + "| NA | 2.78e+00 | 2.35e+00 | 670 | 7.62e+02 |\n", + "+----------------------------+-----------+-------------+---------+----------+\n", + "\n", + "+--------------+----------+----------+-----------------+-----------------+\n", + "| mis.possible | mis.oe | mis.mu | mis.oe_ci.lower | mis.oe_ci.upper |\n", + "+--------------+----------+----------+-----------------+-----------------+\n", + "| int64 | float64 | float64 | float64 | float64 |\n", + "+--------------+----------+----------+-----------------+-----------------+\n", + "| 2870 | 1.09e+00 | 7.67e-06 | 1.03e+00 | 1.16e+00 |\n", + "| 2870 | 1.09e+00 | 7.67e-06 | 1.03e+00 | 1.16e+00 |\n", + "| 1858 | 9.84e-01 | 4.91e-06 | 9.06e-01 | 1.07e+00 |\n", + "| 3720 | 8.76e-01 | 4.38e-06 | 8.21e-01 | 9.34e-01 |\n", + "| 3837 | 8.80e-01 | 4.43e-06 | 8.25e-01 | 9.38e-01 |\n", + "+--------------+----------+----------+-----------------+-----------------+\n", + "\n", + "+-----------+-------------+---------------+---------------+--------------------+\n", + "| mis.z_raw | mis.z_score | mis_pphen.obs | mis_pphen.exp | mis_pphen.possible |\n", + "+-----------+-------------+---------------+---------------+--------------------+\n", + "| float64 | float64 | int64 | float64 | int64 |\n", + "+-----------+-------------+---------------+---------------+--------------------+\n", + "| -2.36e+00 | -8.61e-01 | 220 | 1.91e+02 | 890 |\n", + "| -2.36e+00 | -8.61e-01 | 220 | 1.91e+02 | 890 |\n", + "| 3.24e-01 | 1.18e-01 | 103 | 1.10e+02 | 512 |\n", + "| 3.39e+00 | 1.24e+00 | 236 | 2.77e+02 | 1399 |\n", + "| 3.33e+00 | 1.21e+00 | 309 | 3.65e+02 | 1841 |\n", + "+-----------+-------------+---------------+---------------+--------------------+\n", + "\n", + "+--------------+---------+----------+--------------+----------+----------+\n", + "| mis_pphen.oe | syn.obs | syn.exp | syn.possible | syn.oe | syn.mu |\n", + "+--------------+---------+----------+--------------+----------+----------+\n", + "| float64 | int64 | float64 | int64 | float64 | float64 |\n", + "+--------------+---------+----------+--------------+----------+----------+\n", + "| 1.15e+00 | 316 | 2.96e+02 | 994 | 1.07e+00 | 3.02e-06 |\n", + "| 1.15e+00 | 316 | 2.96e+02 | 994 | 1.07e+00 | 3.02e-06 |\n", + "| 9.40e-01 | 166 | 1.79e+02 | 637 | 9.28e-01 | 1.91e-06 |\n", + "| 8.53e-01 | 272 | 2.73e+02 | 1157 | 9.98e-01 | 2.01e-06 |\n", + "| 8.47e-01 | 271 | 2.76e+02 | 1188 | 9.82e-01 | 1.91e-06 |\n", + "+--------------+---------+----------+--------------+----------+----------+\n", + "\n", + "+-----------------+-----------------+-----------+-------------+\n", + "| syn.oe_ci.lower | syn.oe_ci.upper | syn.z_raw | syn.z_score |\n", + "+-----------------+-----------------+-----------+-------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+-----------------+-----------------+-----------+-------------+\n", + "| 9.73e-01 | 1.17e+00 | -1.17e+00 | -6.35e-01 |\n", + "| 9.73e-01 | 1.17e+00 | -1.17e+00 | -6.35e-01 |\n", + "| 8.17e-01 | 1.06e+00 | 9.64e-01 | 5.26e-01 |\n", + "| 9.03e-01 | 1.10e+00 | 3.27e-02 | 1.78e-02 |\n", + "| 8.88e-01 | 1.09e+00 | 3.00e-01 | 1.64e-01 |\n", + "+-----------------+-----------------+-----------+-------------+\n", + "\n", + "+------------------+-------+------------------+------------+------------+\n", + "| constraint_flags | level | transcript_type | chromosome | cds_length |\n", + "+------------------+-------+------------------+------------+------------+\n", + "| set | str | str | str | int64 |\n", + "+------------------+-------+------------------+------------+------------+\n", + "| {} | NA | NA | NA | NA |\n", + "| {} | \"2\" | \"protein_coding\" | \"chr19\" | 1485 |\n", + "| {} | \"1\" | \"protein_coding\" | \"chr19\" | 917 |\n", + "| {} | NA | NA | NA | NA |\n", + "| {} | NA | NA | NA | NA |\n", + "+------------------+-------+------------------+------------+------------+\n", + "\n", + "+------------------+\n", + "| num_coding_exons |\n", + "+------------------+\n", + "| int64 |\n", + "+------------------+\n", + "| NA |\n", + "| 8 |\n", + "| 5 |\n", + "| NA |\n", + "| NA |\n", + "+------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "72ce8dac-994f-42ae-a6a0-948f5517e427", + "metadata": { + "tags": [] + }, + "source": [ + "## Proportion expressed across transcripts (pext) score\n", + "\n", + "For more information about the pext score see the [Help](https://gnomad.broadinstitute.org/help/pext) page." + ] + }, + { + "cell_type": "markdown", + "id": "ea178a83-1bb6-4f9b-8334-0ca1f6096021", + "metadata": {}, + "source": [ + "### Base level pext\n" + ] + }, + { + "cell_type": "markdown", + "id": "eeb8bb32-d258-4b3a-ab5e-edc76502fa5b", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1f448e05-7f99-4c63-b101-b33143d9d58b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(version='v10', data_type=\"base_level\", dataset=\"pext\")" + ] + }, + { + "cell_type": "markdown", + "id": "0773d5d3-21f1-4892-b16b-dc31eb2a7bf5", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b9f99311-91e6-454e-b940-3d036bddf3a5", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'tissues': array \n", + " 'exp_prop_mean_tissues': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'gene_id': str \n", + " 'gene_symbol': str \n", + " 'exp_prop_mean': float64 \n", + " 'Adipose_Subcutaneous': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Adipose_Visceral_Omentum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'AdrenalGland': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Aorta': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Coronary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Tibial': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Bladder': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Amygdala': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Anteriorcingulatecortex_BA24': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Caudate_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_CerebellarHemisphere': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Cerebellum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Cortex': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_FrontalCortex_BA9': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Hippocampus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Hypothalamus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Nucleusaccumbens_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Putamen_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Spinalcord_cervicalc_1': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Substantianigra': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Breast_MammaryTissue': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Cells_Culturedfibroblasts': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Cells_EBV_transformedlymphocytes': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Colon_Sigmoid': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Colon_Transverse': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_GastroesophagealJunction': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_Mucosa': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_Muscularis': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Heart_AtrialAppendage': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Heart_LeftVentricle': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Kidney_Cortex': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Liver': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Lung': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'MinorSalivaryGland': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Muscle_Skeletal': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Nerve_Tibial': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Ovary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Pancreas': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Pituitary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Prostate': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Skin_NotSunExposed_Suprapubic': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Skin_SunExposed_Lowerleg': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'SmallIntestine_TerminalIleum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Spleen': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Stomach': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Testis': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Thyroid': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Uterus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Vagina': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'WholeBlood': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "a39f9dca-5db4-4ca3-8eac-e6c76db7c7bc", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a6ac695f-cfb2-4aa7-877c-b204351a77da", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
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Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
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Liver
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Muscle_Skeletal
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Ovary
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Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
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expression_proportion
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chr1:65565"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65567"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65568"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65569"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+-------------------+-------------+---------------+\n", + "| locus | gene_id | gene_symbol | exp_prop_mean |\n", + "+---------------+-------------------+-------------+---------------+\n", + "| locus | str | str | float64 |\n", + "+---------------+-------------------+-------------+---------------+\n", + "| chr1:65565 | \"ENSG00000186092\" | \"OR4F5\" | NaN |\n", + "| chr1:65566 | \"ENSG00000186092\" | \"OR4F5\" | NaN |\n", + "| chr1:65567 | \"ENSG00000186092\" | \"OR4F5\" | NaN |\n", + "| chr1:65568 | \"ENSG00000186092\" | \"OR4F5\" | NaN |\n", + "| chr1:65569 | \"ENSG00000186092\" | \"OR4F5\" | NaN |\n", + "+---------------+-------------------+-------------+---------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Adipose_Subcutaneous.transcript_expression |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Adipose_Subcutaneous.expression_proportion |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Adipose_Visceral_Omentum.transcript_expression |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Adipose_Visceral_Omentum.expression_proportion |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+------------------------------------+------------------------------------+\n", + "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n", + "+------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------------+------------------------------------+\n", + "\n", + "+------------------------------------+------------------------------------+\n", + "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n", + "+------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------------+------------------------------------+\n", + "\n", + "+---------------------------------------+\n", + "| Artery_Coronary.transcript_expression |\n", + "+---------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------+\n", + "\n", + "+---------------------------------------+-------------------------------------+\n", + "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n", + "+---------------------------------------+-------------------------------------+\n", + "| float64 | float64 |\n", + "+---------------------------------------+-------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+---------------------------------------+-------------------------------------+\n", + "\n", + "+-------------------------------------+-------------------------------+\n", + "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n", + "+-------------------------------------+-------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------------+-------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------------+-------------------------------+\n", + "\n", + "+-------------------------------+--------------------------------------+\n", + "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n", + "+-------------------------------+--------------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+--------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+--------------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| Brain_Amygdala.expression_proportion |\n", + "+--------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_Caudate_basalganglia.transcript_expression |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_Caudate_basalganglia.expression_proportion |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_CerebellarHemisphere.transcript_expression |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_CerebellarHemisphere.expression_proportion |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+----------------------------------------+\n", + "| Brain_Cerebellum.transcript_expression |\n", + "+----------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------+\n", + "\n", + "+----------------------------------------+------------------------------------+\n", + "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n", + "+----------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+----------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+----------------------------------------+------------------------------------+\n", + "\n", + "+------------------------------------+\n", + "| Brain_Cortex.expression_proportion |\n", + "+------------------------------------+\n", + "| float64 |\n", + "+------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------+\n", + "\n", + "+-----------------------------------------------+\n", + "| Brain_FrontalCortex_BA9.transcript_expression |\n", + "+-----------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------+\n", + "\n", + "+-----------------------------------------------+\n", + "| Brain_FrontalCortex_BA9.expression_proportion |\n", + "+-----------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------+\n", + "\n", + "+-----------------------------------------+\n", + "| Brain_Hippocampus.transcript_expression |\n", + "+-----------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------+\n", + "\n", + "+-----------------------------------------+\n", + "| Brain_Hippocampus.expression_proportion |\n", + "+-----------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------+\n", + "\n", + "+------------------------------------------+\n", + "| Brain_Hypothalamus.transcript_expression |\n", + "+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------+\n", + "\n", + "+------------------------------------------+\n", + "| Brain_Hypothalamus.expression_proportion |\n", + "+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------+\n", + "\n", + "+-----------------------------------------------------------+\n", + "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n", + "+-----------------------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------------------+\n", + "\n", + "+-----------------------------------------------------------+\n", + "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n", + "+-----------------------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_Putamen_basalganglia.transcript_expression |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| Brain_Putamen_basalganglia.expression_proportion |\n", + "+--------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n", + "+----------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n", + "+----------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| Brain_Substantianigra.transcript_expression |\n", + "+---------------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| Brain_Substantianigra.expression_proportion |\n", + "+---------------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Breast_MammaryTissue.transcript_expression |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Breast_MammaryTissue.expression_proportion |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+-------------------------------------------------+\n", + "| Cells_Culturedfibroblasts.transcript_expression |\n", + "+-------------------------------------------------+\n", + "| float64 |\n", + "+-------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-------------------------------------------------+\n", + "\n", + "+-------------------------------------------------+\n", + "| Cells_Culturedfibroblasts.expression_proportion |\n", + "+-------------------------------------------------+\n", + "| float64 |\n", + "+-------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-------------------------------------------------+\n", + "\n", + "+--------------------------------------------------------+\n", + "| Cells_EBV_transformedlymphocytes.transcript_expression |\n", + "+--------------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------------+\n", + "\n", + "+--------------------------------------------------------+\n", + "| Cells_EBV_transformedlymphocytes.expression_proportion |\n", + "+--------------------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------------------+\n", + "\n", + "+-------------------------------------+-------------------------------------+\n", + "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n", + "+-------------------------------------+-------------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------------+-------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------------+-------------------------------------+\n", + "\n", + "+----------------------------------------+\n", + "| Colon_Transverse.transcript_expression |\n", + "+----------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------+\n", + "\n", + "+----------------------------------------+\n", + "| Colon_Transverse.expression_proportion |\n", + "+----------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Esophagus_GastroesophagealJunction.transcript_expression |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Esophagus_GastroesophagealJunction.expression_proportion |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+----------------------------------------+\n", + "| Esophagus_Mucosa.transcript_expression |\n", + "+----------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------+\n", + "\n", + "+----------------------------------------+\n", + "| Esophagus_Mucosa.expression_proportion |\n", + "+----------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Esophagus_Muscularis.transcript_expression |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Esophagus_Muscularis.expression_proportion |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| Heart_AtrialAppendage.transcript_expression |\n", + "+---------------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| Heart_AtrialAppendage.expression_proportion |\n", + "+---------------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| Heart_LeftVentricle.transcript_expression |\n", + "+-------------------------------------------+\n", + "| float64 |\n", + "+-------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-------------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| Heart_LeftVentricle.expression_proportion |\n", + "+-------------------------------------------+\n", + "| float64 |\n", + "+-------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-------------------------------------------+\n", + "\n", + "+-------------------------------------+-------------------------------------+\n", + "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n", + "+-------------------------------------+-------------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------------+-------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------------+-------------------------------------+\n", + "\n", + "+-----------------------------+-----------------------------+\n", + "| Liver.transcript_expression | Liver.expression_proportion |\n", + "+-----------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+-----------------------------+-----------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-----------------------------+-----------------------------+\n", + "\n", + "+----------------------------+----------------------------+\n", + "| Lung.transcript_expression | Lung.expression_proportion |\n", + "+----------------------------+----------------------------+\n", + "| float64 | float64 |\n", + "+----------------------------+----------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+----------------------------+----------------------------+\n", + "\n", + "+------------------------------------------+\n", + "| MinorSalivaryGland.transcript_expression |\n", + "+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------+\n", + "\n", + "+------------------------------------------+\n", + "| MinorSalivaryGland.expression_proportion |\n", + "+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------+\n", + "\n", + "+---------------------------------------+\n", + "| Muscle_Skeletal.transcript_expression |\n", + "+---------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------+\n", + "\n", + "+---------------------------------------+------------------------------------+\n", + "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n", + "+---------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+---------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+---------------------------------------+------------------------------------+\n", + "\n", + "+------------------------------------+-----------------------------+\n", + "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n", + "+------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------+-----------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------------+-----------------------------+\n", + "\n", + "+-----------------------------+--------------------------------+\n", + "| Ovary.expression_proportion | Pancreas.transcript_expression |\n", + "+-----------------------------+--------------------------------+\n", + "| float64 | float64 |\n", + "+-----------------------------+--------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-----------------------------+--------------------------------+\n", + "\n", + "+--------------------------------+---------------------------------+\n", + "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n", + "+--------------------------------+---------------------------------+\n", + "| float64 | float64 |\n", + "+--------------------------------+---------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+--------------------------------+---------------------------------+\n", + "\n", + "+---------------------------------+--------------------------------+\n", + "| Pituitary.expression_proportion | Prostate.transcript_expression |\n", + "+---------------------------------+--------------------------------+\n", + "| float64 | float64 |\n", + "+---------------------------------+--------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+---------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------+\n", + "| Prostate.expression_proportion |\n", + "+--------------------------------+\n", + "| float64 |\n", + "+--------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------+\n", + "\n", + "+-----------------------------------------------------+\n", + "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n", + "+-----------------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------------+\n", + "\n", + "+-----------------------------------------------------+\n", + "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n", + "+-----------------------------------------------------+\n", + "| float64 |\n", + "+-----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+-----------------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Skin_SunExposed_Lowerleg.transcript_expression |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Skin_SunExposed_Lowerleg.expression_proportion |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| SmallIntestine_TerminalIleum.transcript_expression |\n", + "+----------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| SmallIntestine_TerminalIleum.expression_proportion |\n", + "+----------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Spleen.transcript_expression | Spleen.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+-------------------------------+-------------------------------+\n", + "| Stomach.transcript_expression | Stomach.expression_proportion |\n", + "+-------------------------------+-------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+-------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Testis.transcript_expression | Testis.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+-------------------------------+-------------------------------+\n", + "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n", + "+-------------------------------+-------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+-------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Uterus.transcript_expression | Uterus.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Vagina.transcript_expression | Vagina.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+----------------------------------+----------------------------------+\n", + "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n", + "+----------------------------------+----------------------------------+\n", + "| float64 | float64 |\n", + "+----------------------------------+----------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+----------------------------------+----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "32528515-1021-40e4-8f4c-3ca06792e0be", + "metadata": {}, + "source": [ + "### Annotation level pext\n" + ] + }, + { + "cell_type": "markdown", + "id": "afac2ca3-f3dc-4174-aa98-d9eb70bf343f", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ce25b9e0-9118-4bf6-ba3c-eca75468b9f1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(version='v10', data_type=\"annotation_level\", dataset=\"pext\")" + ] + }, + { + "cell_type": "markdown", + "id": "c9265ccc-0048-40f0-8f1c-d62e0d3f98ab", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "818f3fc2-09fa-479c-91e1-e19f5991a22d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'tissues': array \n", + " 'exp_prop_mean_tissues': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'gene_id': str \n", + " 'alleles': array \n", + " 'gene_symbol': str \n", + " 'most_severe_consequence': str \n", + " 'lof': str \n", + " 'lof_flags': str \n", + " 'exp_prop_mean': float64 \n", + " 'Adipose_Subcutaneous': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Adipose_Visceral_Omentum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'AdrenalGland': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Aorta': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Coronary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Artery_Tibial': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Bladder': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Amygdala': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Anteriorcingulatecortex_BA24': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Caudate_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_CerebellarHemisphere': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Cerebellum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Cortex': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_FrontalCortex_BA9': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Hippocampus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Hypothalamus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Nucleusaccumbens_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Putamen_basalganglia': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Spinalcord_cervicalc_1': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Brain_Substantianigra': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Breast_MammaryTissue': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Cells_Culturedfibroblasts': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Cells_EBV_transformedlymphocytes': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Colon_Sigmoid': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Colon_Transverse': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_GastroesophagealJunction': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_Mucosa': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Esophagus_Muscularis': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Heart_AtrialAppendage': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Heart_LeftVentricle': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Kidney_Cortex': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Liver': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Lung': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'MinorSalivaryGland': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Muscle_Skeletal': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Nerve_Tibial': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Ovary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Pancreas': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Pituitary': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Prostate': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Skin_NotSunExposed_Suprapubic': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Skin_SunExposed_Lowerleg': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'SmallIntestine_TerminalIleum': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Spleen': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Stomach': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Testis': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Thyroid': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Uterus': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'Vagina': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + " 'WholeBlood': struct {\n", + " transcript_expression: float64, \n", + " expression_proportion: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "77d8e1eb-d07d-4338-a401-ae9191ad2901", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "35371de7-9332-4aa6-a3b9-c573fb2e144f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
locus
gene_id
alleles
gene_symbol
most_severe_consequence
lof
lof_flags
exp_prop_mean
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
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locus<GRCh38>strarray<str>strstrstrstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092"["A","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","G"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","T"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","A"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+-------------------+------------+-------------+\n", + "| locus | gene_id | alleles | gene_symbol |\n", + "+---------------+-------------------+------------+-------------+\n", + "| locus | str | array | str |\n", + "+---------------+-------------------+------------+-------------+\n", + "| chr1:65565 | \"ENSG00000186092\" | [\"A\",\"C\"] | \"OR4F5\" |\n", + "| chr1:65565 | \"ENSG00000186092\" | [\"A\",\"G\"] | \"OR4F5\" |\n", + "| chr1:65565 | \"ENSG00000186092\" | [\"A\",\"T\"] | \"OR4F5\" |\n", + "| chr1:65566 | \"ENSG00000186092\" | [\"T\",\"A\"] | \"OR4F5\" |\n", + "| chr1:65566 | \"ENSG00000186092\" | [\"T\",\"C\"] | \"OR4F5\" |\n", + "+---------------+-------------------+------------+-------------+\n", + "\n", + "+-------------------------+-----+-----------+---------------+\n", + "| most_severe_consequence | lof | lof_flags | exp_prop_mean |\n", + "+-------------------------+-----+-----------+---------------+\n", + "| str | str | str | float64 |\n", + "+-------------------------+-----+-----------+---------------+\n", + "| \"start_lost\" | NA | NA | NaN |\n", + "| \"start_lost\" | NA | NA | NaN |\n", + "| \"start_lost\" | NA | NA | NaN |\n", + "| \"start_lost\" | NA | NA | NaN |\n", + "| \"start_lost\" | NA | NA | NaN |\n", + "+-------------------------+-----+-----------+---------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Adipose_Subcutaneous.transcript_expression |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| Adipose_Subcutaneous.expression_proportion |\n", + "+--------------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Adipose_Visceral_Omentum.transcript_expression |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+------------------------------------------------+\n", + "| Adipose_Visceral_Omentum.expression_proportion |\n", + "+------------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+------------------------------------------------+\n", + "\n", + "+------------------------------------+------------------------------------+\n", + "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n", + "+------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------------+------------------------------------+\n", + "\n", + "+------------------------------------+------------------------------------+\n", + "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n", + "+------------------------------------+------------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------+------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------------+------------------------------------+\n", + "\n", + "+---------------------------------------+\n", + "| Artery_Coronary.transcript_expression |\n", + "+---------------------------------------+\n", + "| float64 |\n", + "+---------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+---------------------------------------+\n", + "\n", + "+---------------------------------------+-------------------------------------+\n", + "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n", + "+---------------------------------------+-------------------------------------+\n", + "| float64 | float64 |\n", + "+---------------------------------------+-------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+---------------------------------------+-------------------------------------+\n", + "\n", + "+-------------------------------------+-------------------------------+\n", + "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n", + "+-------------------------------------+-------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------------+-------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------------+-------------------------------+\n", + "\n", + "+-------------------------------+--------------------------------------+\n", + "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n", + "+-------------------------------+--------------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+--------------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+--------------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| Brain_Amygdala.expression_proportion |\n", + "+--------------------------------------+\n", + "| float64 |\n", + "+--------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+----------------------------------------------------------+\n", + "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n", + "+----------------------------------------------------------+\n", + "| float64 |\n", + "+----------------------------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+----------------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+\n", + "| 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"| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+-------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Testis.transcript_expression | Testis.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+-------------------------------+-------------------------------+\n", + "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n", + "+-------------------------------+-------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+-------------------------------+-------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Uterus.transcript_expression | Uterus.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+------------------------------+------------------------------+\n", + "| Vagina.transcript_expression | Vagina.expression_proportion |\n", + "+------------------------------+------------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------+------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+------------------------------+------------------------------+\n", + "\n", + "+----------------------------------+----------------------------------+\n", + "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n", + "+----------------------------------+----------------------------------+\n", + "| float64 | float64 |\n", + "+----------------------------------+----------------------------------+\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "| 0.00e+00 | 0.00e+00 |\n", + "+----------------------------------+----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "0d1780f9", + "metadata": { + "tags": [] + }, + "source": [ + "## Lifted over variant data (GRCh37 --> GRCh38)" + ] + }, + { + "cell_type": "markdown", + "id": "93ba18a4", + "metadata": { + "tags": [] + }, + "source": [ + "### Exomes liftover Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "207b8b4a", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "4a29c79d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='2.1.1', dataset=\"liftover\")" + ] + }, + { + "cell_type": "markdown", + "id": "5d9872f9", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "dcd9ad6e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'rf': struct {\n", + " variants_by_type: dict, \n", + " feature_medians: dict, \n", + " test_intervals: array>>, \n", + " test_results: array, \n", + " features_importance: dict, \n", + " features: array, \n", + " vqsr_training: bool, \n", + " no_transmitted_singletons: bool, \n", + " adj: bool, \n", + " rf_hash: str, \n", + " rf_snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " rf_indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }\n", + " } \n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'popmax_index_dict': dict \n", + " 'age_index_dict': dict \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'ReverseComplementedAlleles': bool \n", + " 'SwappedAlleles': bool \n", + " 'original_locus': locus \n", + " 'freq': array \n", + " 'age_hist_het': array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }> \n", + " 'age_hist_hom': array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }> \n", + " 'popmax': array \n", + " 'faf': array, \n", + " faf95: float64, \n", + " faf99: float64\n", + " }> \n", + " 'lcr': bool \n", + " 'decoy': bool \n", + " 'segdup': bool \n", + " 'nonpar': bool \n", + " 'variant_type': str \n", + " 'allele_type': str \n", + " 'n_alt_alleles': int32 \n", + " 'was_mixed': bool \n", + " 'has_star': bool \n", + " 'qd': float64 \n", + " 'pab_max': float64 \n", + " 'info_MQRankSum': float64 \n", + " 'info_SOR': float64 \n", + " 'info_InbreedingCoeff': float64 \n", + " 'info_ReadPosRankSum': float64 \n", + " 'info_FS': float64 \n", + " 'info_QD': float64 \n", + " 'info_MQ': float64 \n", + " 'info_DP': int32 \n", + " 'transmitted_singleton': bool \n", + " 'fail_hard_filters': bool \n", + " 'info_POSITIVE_TRAIN_SITE': bool \n", + " 'info_NEGATIVE_TRAIN_SITE': bool \n", + " 'omni': bool \n", + " 'mills': bool \n", + " 'n_nonref': int32 \n", + " 'tp': bool \n", + " 'rf_train': bool \n", + " 'rf_label': str \n", + " 'rf_probability': float64 \n", + " 'singleton': bool \n", + " 'was_split': bool \n", + " 'score': float64 \n", + " 'rank': int64 \n", + " 'singleton_rank': int64 \n", + " 'biallelic_rank': int64 \n", + " 'adj_biallelic_singleton_rank': int64 \n", + " 'adj_rank': int64 \n", + " 'adj_biallelic_rank': int64 \n", + " 'adj_singleton_rank': int64 \n", + " 'biallelic_singleton_rank': int64 \n", + " 'filters': set \n", + " 'gq_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'gq_hist_all': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'dp_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'dp_hist_all': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'ab_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'qual': float64 \n", + " 'vep': struct {\n", + " assembly_name: str, \n", + " allele_string: str, \n", + " ancestral: str, \n", + " colocated_variants: array, \n", + " end: int32, \n", + " eas_allele: str, \n", + " eas_maf: float64, \n", + " ea_allele: str, \n", + " ea_maf: float64, \n", + " eur_allele: str, \n", + " eur_maf: float64, \n", + " exac_adj_allele: str, \n", + " exac_adj_maf: float64, \n", + " exac_allele: str, \n", + " exac_afr_allele: str, \n", + " exac_afr_maf: float64, \n", + " exac_amr_allele: str, \n", + " exac_amr_maf: float64, \n", + " exac_eas_allele: str, \n", + " exac_eas_maf: float64, \n", + " exac_fin_allele: str, \n", + " exac_fin_maf: float64, \n", + " exac_maf: float64, \n", + " exac_nfe_allele: str, \n", + " exac_nfe_maf: float64, \n", + " exac_oth_allele: str, \n", + " exac_oth_maf: float64, \n", + " exac_sas_allele: str, \n", + " exac_sas_maf: float64, \n", + " id: str, \n", + " minor_allele: str, \n", + " minor_allele_freq: float64, \n", + " phenotype_or_disease: int32, \n", + " pubmed: array, \n", + " sas_allele: str, \n", + " sas_maf: float64, \n", + " somatic: int32, \n", + " start: int32, \n", + " strand: int32\n", + " }>, \n", + " context: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " minimised: int32, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " minimised: int32, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " minimised: int32, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " minimised: int32, \n", + " polyphen_prediction: str, \n", + " polyphen_score: float64, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " sift_prediction: str, \n", + " sift_score: float64, \n", + " strand: int32, \n", + " swissprot: str, \n", + " transcript_id: str, \n", + " trembl: str, \n", + " uniparc: str, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'allele_info': struct {\n", + " BaseQRankSum: float64, \n", + " ClippingRankSum: float64, \n", + " DB: bool, \n", + " DP: int32, \n", + " DS: bool, \n", + " END: int32, \n", + " FS: float64, \n", + " HaplotypeScore: float64, \n", + " InbreedingCoeff: float64, \n", + " MQ: float64, \n", + " MQRankSum: float64, \n", + " NEGATIVE_TRAIN_SITE: bool, \n", + " POSITIVE_TRAIN_SITE: bool, \n", + " QD: float64, \n", + " ReadPosRankSum: float64, \n", + " SOR: float64, \n", + " VQSLOD: float64, \n", + " culprit: str\n", + " } \n", + " 'rsid': str \n", + " 'original_alleles': array \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "7305a806", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "04b6da53", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
gq_hist_alt
gq_hist_all
dp_hist_alt
dp_hist_all
ab_hist_alt
vep
allele_info
locus
alleles
ReverseComplementedAlleles
SwappedAlleles
original_locus
freq
age_hist_het
age_hist_hom
popmax
faf
lcr
decoy
segdup
nonpar
variant_type
allele_type
n_alt_alleles
was_mixed
has_star
qd
pab_max
info_MQRankSum
info_SOR
info_InbreedingCoeff
info_ReadPosRankSum
info_FS
info_QD
info_MQ
info_DP
transmitted_singleton
fail_hard_filters
info_POSITIVE_TRAIN_SITE
info_NEGATIVE_TRAIN_SITE
omni
mills
n_nonref
tp
rf_train
rf_label
rf_probability
singleton
was_split
score
rank
singleton_rank
biallelic_rank
adj_biallelic_singleton_rank
adj_rank
adj_biallelic_rank
adj_singleton_rank
biallelic_singleton_rank
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
qual
assembly_name
allele_string
ancestral
colocated_variants
context
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
BaseQRankSum
ClippingRankSum
DB
DP
DS
END
FS
HaplotypeScore
InbreedingCoeff
MQ
MQRankSum
NEGATIVE_TRAIN_SITE
POSITIVE_TRAIN_SITE
QD
ReadPosRankSum
SOR
VQSLOD
culprit
rsid
original_alleles
locus<GRCh38>array<str>boolboollocus<GRCh37>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32, pop: str}>array<struct{meta: dict<str, str>, faf95: float64, faf99: float64}>boolboolboolboolstrstrint32boolboolfloat64float64float64float64float64float64float64float64float64int32boolboolboolboolboolboolint32boolboolstrfloat64boolboolfloat64int64int64int64int64int64int64int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float64strstrstrarray<struct{aa_allele: str, aa_maf: float64, afr_allele: str, afr_maf: float64, allele_string: str, amr_allele: str, amr_maf: float64, clin_sig: array<str>, end: int32, eas_allele: str, eas_maf: float64, ea_allele: str, ea_maf: float64, eur_allele: str, eur_maf: float64, exac_adj_allele: str, exac_adj_maf: float64, exac_allele: str, exac_afr_allele: str, exac_afr_maf: float64, exac_amr_allele: str, exac_amr_maf: float64, exac_eas_allele: str, exac_eas_maf: float64, exac_fin_allele: str, exac_fin_maf: float64, exac_maf: float64, exac_nfe_allele: str, exac_nfe_maf: float64, exac_oth_allele: str, exac_oth_maf: float64, exac_sas_allele: str, exac_sas_maf: float64, id: str, minor_allele: str, minor_allele_freq: float64, phenotype_or_disease: int32, pubmed: array<int32>, sas_allele: str, sas_maf: float64, somatic: int32, start: int32, strand: int32}>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, minimised: int32, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, minimised: int32, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, minimised: int32, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, minimised: int32, polyphen_prediction: str, polyphen_score: float64, protein_end: int32, protein_start: int32, protein_id: str, sift_prediction: str, sift_score: float64, strand: int32, swissprot: str, transcript_id: str, trembl: str, uniparc: str, variant_allele: str}>strfloat64float64boolint32boolint32float64float64float64float64float64boolboolfloat64float64float64float64strstrarray<str>
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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+----------------------------+----------------+\n", + "| locus | alleles | ReverseComplementedAlleles | SwappedAlleles |\n", + "+---------------+------------+----------------------------+----------------+\n", + "| locus | array | bool | bool |\n", + "+---------------+------------+----------------------------+----------------+\n", + "| chr1:12198 | [\"G\",\"C\"] | False | False |\n", + "| chr1:12237 | [\"G\",\"A\"] | False | False |\n", + "| chr1:12259 | [\"G\",\"C\"] | False | False |\n", + "| chr1:12266 | [\"G\",\"A\"] | False | False |\n", + "| chr1:12272 | [\"G\",\"A\"] | False | False |\n", + "+---------------+------------+----------------------------+----------------+\n", + "\n", + "+----------------+\n", + "| original_locus |\n", + "+----------------+\n", + "| locus |\n", + "+----------------+\n", + "| 1:12198 |\n", + "| 1:12237 |\n", + "| 1:12259 |\n", + "| 1:12266 |\n", + "| 1:12272 |\n", + "+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(0,NA,0,0),(227,4.57e-02,4966,90),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA... |\n", + "| [(0,NA,0,0),(5,4.41e-04,11338,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0... |\n", + "| [(0,0.00e+00,2,0),(2,1.56e-04,12838,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(... |\n", + "| [(0,NA,0,0),(51,4.35e-03,11732,15),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA... |\n", + "| [(0,0.00e+00,2,0),(49,4.30e-03,11392,14),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| age_hist_het |\n", + "+------------------------------------------------------------------------------+\n", + "| array, bin_freq: array, n_smaller... |\n", + "+------------------------------------------------------------------------------+\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| age_hist_hom |\n", + "+------------------------------------------------------------------------------+\n", + "| array, bin_freq: array, n_smaller... |\n", + "+------------------------------------------------------------------------------+\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| popmax |\n", + "+------------------------------------------------------------------------------+\n", + "| array, faf95: float64, faf99: float64}> |\n", + "+------------------------------------------------------------------------------+\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------+-------+--------+--------+--------------+-------------+---------------+\n", + "| lcr | decoy | segdup | nonpar | variant_type | allele_type | n_alt_alleles |\n", + "+-------+-------+--------+--------+--------------+-------------+---------------+\n", + "| bool | bool | bool | bool | str | str | int32 |\n", + "+-------+-------+--------+--------+--------------+-------------+---------------+\n", + "| False | False | True | False | \"snv\" | \"snv\" | 1 |\n", + "| False | False | True | False | \"snv\" | \"snv\" | 1 |\n", + "| False | False | True | False | \"snv\" | \"snv\" | 1 |\n", + "| False | False | True | False | \"snv\" | \"snv\" | 1 |\n", + "| False | False | True | False | \"snv\" | \"snv\" | 1 |\n", + "+-------+-------+--------+--------+--------------+-------------+---------------+\n", + "\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| was_mixed | has_star | qd | pab_max | info_MQRankSum | info_SOR |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| bool | bool | float64 | float64 | float64 | float64 |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| False | False | 2.03e+01 | 1.00e+00 | 7.36e-01 | 3.02e-01 |\n", + "| False | False | 1.55e+01 | 1.00e+00 | -3.58e-01 | 1.29e+00 |\n", + "| False | False | 2.50e+01 | NA | NA | 6.93e-01 |\n", + "| False | False | 2.57e+01 | 1.00e+00 | -7.27e-01 | 2.03e-01 |\n", + "| False | False | 2.65e+01 | 1.00e+00 | 0.00e+00 | 1.96e-01 |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| info_InbreedingCoeff | info_ReadPosRankSum | info_FS | info_QD | info_MQ |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| float64 | float64 | float64 | float64 | float64 |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| 9.80e-03 | 7.36e-01 | 0.00e+00 | 1.40e+01 | 2.30e+01 |\n", + "| -1.44e-01 | 3.58e-01 | 0.00e+00 | 4.31e+00 | 2.20e+01 |\n", + "| -1.42e-01 | NA | 0.00e+00 | 9.36e+00 | 2.00e+01 |\n", + "| -1.38e-01 | 4.06e-01 | 0.00e+00 | 1.61e+01 | 2.23e+01 |\n", + "| -1.34e-01 | 0.00e+00 | 0.00e+00 | 1.69e+01 | 2.23e+01 |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "\n", + "+---------+-----------------------+-------------------+\n", + "| info_DP | transmitted_singleton | fail_hard_filters |\n", + "+---------+-----------------------+-------------------+\n", + "| int32 | bool | bool |\n", + "+---------+-----------------------+-------------------+\n", + "| 9204 | NA | True |\n", + "| 16096 | NA | True |\n", + "| 18814 | NA | True |\n", + "| 18372 | NA | True |\n", + "| 17685 | NA | True |\n", + "+---------+-----------------------+-------------------+\n", + "\n", + "+--------------------------+--------------------------+-------+-------+\n", + "| info_POSITIVE_TRAIN_SITE | info_NEGATIVE_TRAIN_SITE | omni | mills |\n", + "+--------------------------+--------------------------+-------+-------+\n", + "| bool | bool | bool | bool |\n", + "+--------------------------+--------------------------+-------+-------+\n", + "| NA | NA | False | False |\n", + "| NA | NA | False | False |\n", + "| NA | NA | False | False |\n", + "| NA | NA | False | False |\n", + "| NA | NA | False | False |\n", + "+--------------------------+--------------------------+-------+-------+\n", + "\n", + "+----------+-------+----------+----------+----------------+-----------+\n", + "| n_nonref | tp | rf_train | rf_label | rf_probability | singleton |\n", + "+----------+-------+----------+----------+----------------+-----------+\n", + "| int32 | bool | bool | str | float64 | bool |\n", + "+----------+-------+----------+----------+----------------+-----------+\n", + "| 231 | False | True | \"FP\" | 8.37e-01 | False |\n", + "| 5 | False | True | \"FP\" | 4.33e-01 | False |\n", + "| 2 | False | True | \"FP\" | 3.76e-01 | False |\n", + "| 51 | False | True | \"FP\" | 3.75e-01 | False |\n", + "| 49 | False | True | \"FP\" | 3.69e-01 | False |\n", + "+----------+-------+----------+----------+----------------+-----------+\n", + "\n", + "+-----------+----------+----------+----------------+----------------+\n", + "| was_split | score | rank | singleton_rank | biallelic_rank |\n", + "+-----------+----------+----------+----------------+----------------+\n", + "| bool | float64 | int64 | int64 | int64 |\n", + "+-----------+----------+----------+----------------+----------------+\n", + "| False | 8.37e-01 | 6571357 | NA | 4342152 |\n", + "| False | 4.33e-01 | 13350857 | NA | 9360383 |\n", + "| False | 3.76e-01 | 13704393 | NA | 9608904 |\n", + "| False | 3.75e-01 | 13712337 | NA | 9614461 |\n", + "| False | 3.69e-01 | 13743987 | NA | 9634223 |\n", + "+-----------+----------+----------+----------------+----------------+\n", + "\n", + "+------------------------------+----------+--------------------+\n", + "| adj_biallelic_singleton_rank | adj_rank | adj_biallelic_rank |\n", + "+------------------------------+----------+--------------------+\n", + "| int64 | int64 | int64 |\n", + 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[1898,511,26,28,8,4,0,5,2,0,0,0,1,0,0,0,0,0,0,0] | 0 |\n", + "| [3635,1739,178,101,14,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [3978,2020,252,143,22,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [3833,1709,188,102,11,4,0,2,5,0,1,1,1,3,0,6,0,0,0,0] | 0 |\n", + "| [3792,1624,162,87,9,2,1,2,5,0,1,1,1,3,0,6,0,0,0,0] | 0 |\n", + "+------------------------------------------------------+-----------------------+\n", + "\n", + "+----------------------+\n", + "| gq_hist_all.n_larger |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| dp_hist_alt.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+-----------------------+\n", + "| dp_hist_alt.bin_freq | dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+---------------------------------------------+-----------------------+\n", + "| [130,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| 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[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------------------+-----------------------+\n", + "| dp_hist_all.bin_freq | dp_hist_all.n_smaller |\n", + "+--------------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+--------------------------------------------------+-----------------------+\n", + "| [125724,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [125622,126,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [125566,181,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [125584,163,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [125601,146,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 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[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+-----------------------+\n", + "| ab_hist_alt.bin_freq | ab_hist_alt.n_smaller |\n", + "+---------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+---------------------------------------------+-----------------------+\n", + "| [0,0,0,0,1,0,2,0,2,0,10,0,1,28,0,3,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,2,0,0,2,0,9,0,0,8,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,0,1,0,3,0,10,0,0,7,0,0,0,0,0,0] | 0 |\n", + "+---------------------------------------------+-----------------------+\n", + "\n", + "+----------------------+----------+-------------------+-------------------+\n", + "| ab_hist_alt.n_larger | qual | vep.assembly_name | vep.allele_string |\n", + "+----------------------+----------+-------------------+-------------------+\n", + "| int64 | float64 | str | str |\n", + "+----------------------+----------+-------------------+-------------------+\n", + "| 0 | 9.88e+03 | \"GRCh37\" | \"G/C\" |\n", + "| 0 | 8.20e+01 | \"GRCh37\" | \"G/A\" |\n", + "| 0 | 3.74e+01 | \"GRCh37\" | \"G/C\" |\n", + "| 0 | 2.72e+03 | \"GRCh37\" | \"G/A\" |\n", + "| 0 | 2.71e+03 | \"GRCh37\" | \"G/A\" |\n", + "+----------------------+----------+-------------------+-------------------+\n", + "\n", + "+---------------+\n", + "| vep.ancestral |\n", + "+---------------+\n", + "| str |\n", + "+---------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.colocated_variants |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| vep.most_severe_consequence |\n", + "+--------------------------------------+\n", + "| str |\n", + "+--------------------------------------+\n", + "| \"non_coding_transcript_exon_variant\" |\n", + "| \"intron_variant\" |\n", + "| \"intron_variant\" |\n", + "| \"intron_variant\" |\n", + "| \"intron_variant\" |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.motif_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, high_inf_p... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.regulatory_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+---------------------+----------------+------------------+\n", + "| \"MQ\" | \"rs62635282\" | [\"G\",\"C\"] |\n", + "| \"QD\" | \"rs1324090652\" | [\"G\",\"A\"] |\n", + "| \"MQ\" | \"rs1330604035\" | [\"G\",\"C\"] |\n", + "| \"MQ\" | \"rs1442951560\" | [\"G\",\"A\"] |\n", + "| \"MQ\" | \"rs1281272113\" | [\"G\",\"A\"] |\n", + "+---------------------+----------------+------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "d211d0f0", + "metadata": { + "tags": [] + }, + "source": [ + "### Genomes liftover Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "fcb791b6", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "248e904e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='2.1.1', dataset=\"liftover\")" + ] + }, + { + "cell_type": "markdown", + "id": "620cca6f", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "f718adea", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'rf': struct {\n", + " variants_by_type: dict, \n", + " feature_medians: dict, \n", + " test_intervals: array>>, \n", + " test_results: array, \n", + " features_importance: dict, \n", + " features: array, \n", + " vqsr_training: bool, \n", + " no_transmitted_singletons: bool, \n", + " adj: bool, \n", + " rf_hash: str, \n", + " rf_snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " rf_indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }\n", + " } \n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'popmax_index_dict': dict \n", + " 'age_index_dict': dict \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'ReverseComplementedAlleles': bool \n", + " 'SwappedAlleles': bool \n", + " 'original_locus': locus \n", + " 'freq': array \n", + " 'age_hist_het': array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }> \n", + " 'age_hist_hom': array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }> \n", + " 'popmax': array \n", + " 'faf': array, \n", + " faf95: float64, \n", + " faf99: float64\n", + " }> \n", + " 'lcr': bool \n", + " 'decoy': bool \n", + " 'segdup': bool \n", + " 'nonpar': bool \n", + " 'variant_type': str \n", + " 'allele_type': str \n", + " 'n_alt_alleles': int32 \n", + " 'was_mixed': bool \n", + " 'has_star': bool \n", + " 'qd': float64 \n", + " 'pab_max': float64 \n", + " 'info_MQRankSum': float64 \n", + " 'info_SOR': float64 \n", + " 'info_InbreedingCoeff': float64 \n", + " 'info_ReadPosRankSum': float64 \n", + " 'info_FS': float64 \n", + " 'info_QD': float64 \n", + " 'info_MQ': float64 \n", + " 'info_DP': int32 \n", + " 'transmitted_singleton': bool \n", + " 'fail_hard_filters': bool \n", + " 'info_POSITIVE_TRAIN_SITE': bool \n", + " 'info_NEGATIVE_TRAIN_SITE': bool \n", + " 'omni': bool \n", + " 'mills': bool \n", + " 'tp': bool \n", + " 'rf_train': bool \n", + " 'rf_label': str \n", + " 'rf_probability': float64 \n", + " 'rank': int64 \n", + " 'was_split': bool \n", + " 'singleton': bool \n", + " '_score': float64 \n", + " '_singleton': bool \n", + " 'biallelic_rank': int64 \n", + " 'singleton_rank': int64 \n", + " 'n_nonref': int32 \n", + " 'score': float64 \n", + " 'adj_biallelic_singleton_rank': int64 \n", + " 'adj_rank': int64 \n", + " 'adj_biallelic_rank': int64 \n", + " 'adj_singleton_rank': int64 \n", + " 'biallelic_singleton_rank': int64 \n", + " 'filters': set \n", + " 'gq_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'gq_hist_all': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'dp_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'dp_hist_all': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'ab_hist_alt': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " } \n", + " 'qual': float64 \n", + " 'vep': struct {\n", + " assembly_name: str, \n", + " allele_string: str, \n", + " ancestral: str, \n", + " colocated_variants: array, \n", + " end: int32, \n", + " eas_allele: str, \n", + " eas_maf: float64, \n", + " ea_allele: str, \n", + " ea_maf: float64, \n", + " eur_allele: str, \n", + " eur_maf: float64, \n", + " exac_adj_allele: str, \n", + " exac_adj_maf: float64, \n", + " exac_allele: str, \n", + " exac_afr_allele: str, \n", + " exac_afr_maf: float64, \n", + " exac_amr_allele: str, \n", + " exac_amr_maf: float64, \n", + " exac_eas_allele: str, \n", + " exac_eas_maf: float64, \n", + " exac_fin_allele: str, \n", + " exac_fin_maf: float64, \n", + " exac_maf: float64, \n", + " exac_nfe_allele: str, \n", + " exac_nfe_maf: float64, \n", + " exac_oth_allele: str, \n", + " exac_oth_maf: float64, \n", + " exac_sas_allele: str, \n", + " exac_sas_maf: float64, \n", + " id: str, \n", + " minor_allele: str, \n", + " minor_allele_freq: float64, \n", + " phenotype_or_disease: int32, \n", + " pubmed: array, \n", + " sas_allele: str, \n", + " sas_maf: float64, \n", + " somatic: int32, \n", + " start: int32, \n", + " strand: int32\n", + " }>, \n", + " context: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " minimised: int32, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " minimised: int32, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " minimised: int32, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " minimised: int32, \n", + " polyphen_prediction: str, \n", + " polyphen_score: float64, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " sift_prediction: str, \n", + " sift_score: float64, \n", + " strand: int32, \n", + " swissprot: str, \n", + " transcript_id: str, \n", + " trembl: str, \n", + " uniparc: str, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'allele_info': struct {\n", + " BaseQRankSum: float64, \n", + " ClippingRankSum: float64, \n", + " DB: bool, \n", + " DP: int32, \n", + " DS: bool, \n", + " END: int32, \n", + " FS: float64, \n", + " HaplotypeScore: float64, \n", + " InbreedingCoeff: float64, \n", + " MQ: float64, \n", + " MQ0: int32, \n", + " MQRankSum: float64, \n", + " NEGATIVE_TRAIN_SITE: bool, \n", + " POSITIVE_TRAIN_SITE: bool, \n", + " QD: float64, \n", + " RAW_MQ: float64, \n", + " ReadPosRankSum: float64, \n", + " SOR: float64, \n", + " VQSLOD: float64, \n", + " culprit: str\n", + " } \n", + " 'rsid': str \n", + " 'original_alleles': array \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "ab2f106a", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "0a9ac872", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
gq_hist_alt
gq_hist_all
dp_hist_alt
dp_hist_all
ab_hist_alt
vep
allele_info
locus
alleles
ReverseComplementedAlleles
SwappedAlleles
original_locus
freq
age_hist_het
age_hist_hom
popmax
faf
lcr
decoy
segdup
nonpar
variant_type
allele_type
n_alt_alleles
was_mixed
has_star
qd
pab_max
info_MQRankSum
info_SOR
info_InbreedingCoeff
info_ReadPosRankSum
info_FS
info_QD
info_MQ
info_DP
transmitted_singleton
fail_hard_filters
info_POSITIVE_TRAIN_SITE
info_NEGATIVE_TRAIN_SITE
omni
mills
tp
rf_train
rf_label
rf_probability
rank
was_split
singleton
_score
_singleton
biallelic_rank
singleton_rank
n_nonref
score
adj_biallelic_singleton_rank
adj_rank
adj_biallelic_rank
adj_singleton_rank
biallelic_singleton_rank
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
qual
assembly_name
allele_string
ancestral
colocated_variants
context
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
BaseQRankSum
ClippingRankSum
DB
DP
DS
END
FS
HaplotypeScore
InbreedingCoeff
MQ
MQ0
MQRankSum
NEGATIVE_TRAIN_SITE
POSITIVE_TRAIN_SITE
QD
RAW_MQ
ReadPosRankSum
SOR
VQSLOD
culprit
rsid
original_alleles
locus<GRCh38>array<str>boolboollocus<GRCh37>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32, pop: str}>array<struct{meta: dict<str, str>, faf95: float64, faf99: float64}>boolboolboolboolstrstrint32boolboolfloat64float64float64float64float64float64float64float64float64int32boolboolboolboolboolboolboolboolstrfloat64int64boolboolfloat64boolint64int64int32float64int64int64int64int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float64strstrstrarray<struct{aa_allele: str, aa_maf: float64, afr_allele: str, afr_maf: float64, allele_string: str, amr_allele: str, amr_maf: float64, clin_sig: array<str>, end: int32, eas_allele: str, eas_maf: float64, ea_allele: str, ea_maf: float64, eur_allele: str, eur_maf: float64, exac_adj_allele: str, exac_adj_maf: float64, exac_allele: str, exac_afr_allele: str, exac_afr_maf: float64, exac_amr_allele: str, exac_amr_maf: float64, exac_eas_allele: str, exac_eas_maf: float64, exac_fin_allele: str, exac_fin_maf: float64, exac_maf: float64, exac_nfe_allele: str, exac_nfe_maf: float64, exac_oth_allele: str, exac_oth_maf: float64, exac_sas_allele: str, exac_sas_maf: float64, id: str, minor_allele: str, minor_allele_freq: float64, phenotype_or_disease: int32, pubmed: array<int32>, sas_allele: str, sas_maf: float64, somatic: int32, start: int32, strand: int32}>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, minimised: int32, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, minimised: int32, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, minimised: int32, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, minimised: int32, polyphen_prediction: str, polyphen_score: float64, protein_end: int32, protein_start: int32, protein_id: str, sift_prediction: str, sift_score: float64, strand: int32, swissprot: str, transcript_id: str, trembl: str, uniparc: str, variant_allele: str}>strfloat64float64boolint32boolint32float64float64float64float64int32float64boolboolfloat64float64float64float64float64strstrarray<str>
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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| chr1:10067 |\n", + "| chr1:10108 |\n", + "| chr1:10109 |\n", + "| chr1:10114 |\n", + "| chr1:10114 |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| alleles |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [\"T\",\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\"] |\n", + "| [\"CAACCCT\",\"C\"] |\n", + "| [\"AACCCT\",\"A\"] |\n", + "| [\"T\",\"C\"] |\n", + "| [\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACC... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+----------------------------+----------------+----------------+\n", + "| ReverseComplementedAlleles | SwappedAlleles | original_locus |\n", + "+----------------------------+----------------+----------------+\n", + "| bool | bool | locus |\n", + "+----------------------------+----------------+----------------+\n", + "| False | False | 1:10067 |\n", + "| False | False | 1:10108 |\n", + "| False | False | 1:10109 |\n", + "| False | False | 1:10114 |\n", + "| False | False | 1:10114 |\n", + "+----------------------------+----------------+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(1,3.80e-05,26342,0),(3,9.55e-05,31414,0),(0,0.00e+00,13124,0),(1,3.75e-... |\n", + "| [(2,1.01e-03,1982,0),(2,6.49e-05,30832,0),(0,0.00e+00,1452,0),(1,2.27e-02... |\n", + "| [(19,6.42e-02,296,0),(31,1.01e-03,30766,0),(8,5.13e-02,156,0),(2,5.88e-02... |\n", + "| [(0,0.00e+00,8734,0),(8,2.56e-04,31192,0),(0,0.00e+00,4054,0),(0,0.00e+00... |\n", + "| [(1,1.14e-04,8734,0),(1,3.21e-05,31192,0),(0,0.00e+00,4054,0),(0,0.00e+00... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| age_hist_het |\n", + "+------------------------------------------------------------------------------+\n", + "| array, bin_freq: array, n_smaller... |\n", + "+------------------------------------------------------------------------------+\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| age_hist_hom |\n", + "+------------------------------------------------------------------------------+\n", + "| array, bin_freq: array, n_smaller... |\n", + "+------------------------------------------------------------------------------+\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| popmax |\n", + "+------------------------------------------------------------------------------+\n", + "| array, faf95: float64, faf99: float64}> |\n", + "+------------------------------------------------------------------------------+\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},1.79e-04,1.79e-04),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},4.20e-02,4.20e-02),({\"group\":\"adj\",\"pop\":\"nfe\"},2.55e-0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------+-------+--------+--------+--------------+-------------+---------------+\n", + "| lcr | decoy | segdup | nonpar | variant_type | allele_type | n_alt_alleles |\n", + "+------+-------+--------+--------+--------------+-------------+---------------+\n", + "| bool | bool | bool | bool | str | str | int32 |\n", + "+------+-------+--------+--------+--------------+-------------+---------------+\n", + "| True | False | True | False | \"indel\" | \"ins\" | 1 |\n", + "| True | False | True | False | \"indel\" | \"del\" | 1 |\n", + "| True | False | True | False | \"indel\" | \"del\" | 1 |\n", + "| True | False | True | False | \"mixed\" | \"snv\" | 2 |\n", + "| True | False | True | False | \"mixed\" | \"del\" | 2 |\n", + "+------+-------+--------+--------+--------------+-------------+---------------+\n", + "\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| was_mixed | has_star | qd | pab_max | info_MQRankSum | info_SOR |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| bool | bool | float64 | float64 | float64 | float64 |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "| False | False | 6.43e+00 | 1.00e+00 | 7.36e-01 | 4.21e-01 |\n", + "| False | False | 4.66e+00 | 1.80e-01 | 2.08e-01 | 8.55e-01 |\n", + "| False | True | 1.83e+00 | 1.00e+00 | -6.93e-01 | 4.88e+00 |\n", + "| True | True | 1.15e+00 | 2.49e-03 | -1.23e+00 | 2.51e+00 |\n", + "| True | True | 5.60e+00 | 4.08e-03 | -1.23e+00 | 2.51e+00 |\n", + "+-----------+----------+----------+----------+----------------+----------+\n", + "\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| info_InbreedingCoeff | info_ReadPosRankSum | info_FS | info_QD | info_MQ |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| float64 | float64 | float64 | float64 | float64 |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "| -3.52e-02 | -7.27e-01 | 0.00e+00 | 1.38e+00 | 3.93e+01 |\n", + "| -3.00e-01 | 1.27e+00 | 1.90e+00 | 3.42e+01 | 3.96e+01 |\n", + "| 1.09e-01 | -9.31e-01 | 5.76e+01 | 3.06e+01 | 3.70e+01 |\n", + "| 2.08e-01 | -9.11e-01 | 2.19e+01 | 2.01e+01 | 3.33e+01 |\n", + "| 2.08e-01 | -9.11e-01 | 2.19e+01 | 2.01e+01 | 3.33e+01 |\n", + "+----------------------+---------------------+----------+----------+----------+\n", + "\n", + "+---------+-----------------------+-------------------+\n", + "| info_DP | transmitted_singleton | fail_hard_filters |\n", + "+---------+-----------------------+-------------------+\n", + "| int32 | bool | bool |\n", + "+---------+-----------------------+-------------------+\n", + "| 2040956 | NA | True |\n", + "| 2147991 | NA | False |\n", + "| 2134891 | NA | False |\n", + "| 1908083 | NA | False |\n", + "| 1908083 | NA | False |\n", + "+---------+-----------------------+-------------------+\n", + "\n", + "+--------------------------+--------------------------+-------+-------+-------+\n", + "| info_POSITIVE_TRAIN_SITE | info_NEGATIVE_TRAIN_SITE | omni | mills | tp |\n", + "+--------------------------+--------------------------+-------+-------+-------+\n", + "| bool | bool | bool | bool | bool |\n", + "+--------------------------+--------------------------+-------+-------+-------+\n", + "| NA | NA | False | False | False |\n", + "| NA | NA | False | False | False |\n", + "| NA | NA | False | False | False |\n", + "| NA | NA | False | False | False |\n", + "| NA | NA | False | False | False |\n", + "+--------------------------+--------------------------+-------+-------+-------+\n", + "\n", + "+----------+----------+----------------+-----------+-----------+-----------+\n", + "| rf_train | rf_label | rf_probability | rank | was_split | singleton |\n", + "+----------+----------+----------------+-----------+-----------+-----------+\n", + "| bool | str | float64 | int64 | bool | bool |\n", + "+----------+----------+----------------+-----------+-----------+-----------+\n", + "| True | \"FP\" | 1.49e-01 | 29846037 | False | False |\n", + "| NA | NA | 5.10e-02 | 31936632 | False | False |\n", + "| NA | NA | 4.41e-03 | 34836964 | True | False |\n", + "| NA | NA | 5.81e-03 | 243227706 | True | False |\n", + "| NA | NA | 7.79e-03 | 34144024 | True | True |\n", + "+----------+----------+----------------+-----------+-----------+-----------+\n", + "\n", + "+----------+------------+----------------+----------------+----------+\n", + "| _score | _singleton | biallelic_rank | singleton_rank | n_nonref |\n", + "+----------+------------+----------------+----------------+----------+\n", + "| float64 | bool | int64 | int64 | int32 |\n", + "+----------+------------+----------------+----------------+----------+\n", + "| 8.51e-01 | False | 15142389 | NA | 3 |\n", + "| 9.49e-01 | False | 15649487 | NA | 5 |\n", + "| 9.96e-01 | False | NA | NA | 44 |\n", + "| 9.94e-01 | False | NA | NA | 9 |\n", + "| 9.92e-01 | True | NA | 10987969 | 1 |\n", + "+----------+------------+----------------+----------------+----------+\n", + "\n", + "+----------+------------------------------+----------+--------------------+\n", + "| score | adj_biallelic_singleton_rank | adj_rank | adj_biallelic_rank |\n", + "+----------+------------------------------+----------+--------------------+\n", + "| float64 | int64 | int64 | int64 |\n", + "+----------+------------------------------+----------+--------------------+\n", + "| 1.49e-01 | NA | 27460496 | 13856404 |\n", + "| 5.10e-02 | NA | 29036786 | 14252110 |\n", + "| 4.41e-03 | NA | 31102141 | NA |\n", + "| 5.81e-03 | NA | NA | NA |\n", + "| 7.79e-03 | NA | 30615843 | NA |\n", + "+----------+------------------------------+----------+--------------------+\n", + "\n", + "+--------------------+--------------------------+--------------+\n", + "| adj_singleton_rank | biallelic_singleton_rank | filters |\n", + "+--------------------+--------------------------+--------------+\n", + "| int64 | int64 | set |\n", + "+--------------------+--------------------------+--------------+\n", + "| NA | NA | {\"RF\"} |\n", + "| NA | NA | {\"RF\"} |\n", + "| NA | NA | {\"RF\"} |\n", + "| NA | NA | {\"AC0\",\"RF\"} |\n", + "| 9632693 | NA | {\"RF\"} |\n", + "+--------------------+--------------------------+--------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| gq_hist_alt.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------------------------------------------+-----------------------+\n", + "| gq_hist_alt.bin_freq | gq_hist_alt.n_smaller |\n", + "+-------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+-------------------------------------------+-----------------------+\n", + "| [0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1] | 0 |\n", + "| [0,4,2,1,1,7,2,2,1,1,0,1,1,1,1,0,1,0,0,5] | 0 |\n", + "| [1,2,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1] | 0 |\n", + "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0] | 0 |\n", + "+-------------------------------------------+-----------------------+\n", + "\n", + "+----------------------+\n", + "| gq_hist_alt.n_larger |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| gq_hist_all.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| gq_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [1949,163,179,242,270,286,314,378,357,448,611,483,1069,368,826,491,1015,2... |\n", + "| [14116,107,108,94,86,90,88,83,56,64,76,43,58,32,33,35,51,27,38,131] |\n", + "| [14814,196,99,120,70,27,18,7,4,6,3,2,1,2,2,0,1,0,0,11] |\n", + "| [8661,775,603,1162,1158,559,798,516,193,244,176,93,180,37,65,45,90,12,62,... |\n", + "| [8659,772,603,1160,1157,557,798,516,193,244,174,92,180,36,64,44,91,10,62,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------+----------------------+\n", + "| gq_hist_all.n_smaller | gq_hist_all.n_larger |\n", + "+-----------------------+----------------------+\n", + "| int64 | int64 |\n", + "+-----------------------+----------------------+\n", + "| 0 | 0 |\n", + "| 0 | 0 |\n", + "| 0 | 0 |\n", + "| 0 | 0 |\n", + "| 0 | 0 |\n", + "+-----------------------+----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| dp_hist_alt.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------------------------------------------+-----------------------+\n", + "| dp_hist_alt.bin_freq | dp_hist_alt.n_smaller |\n", + "+-------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+-------------------------------------------+-----------------------+\n", + "| [2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,1,1,4,7,7,4,3,2,2,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,1,4,1,1,0,1,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "+-------------------------------------------+-----------------------+\n", + "\n", + "+----------------------+\n", + "| dp_hist_alt.n_larger |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| dp_hist_all.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| dp_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [3,3,8,18,30,55,95,190,324,554,834,1132,1343,1372,1367,1295,1125,925,766,... |\n", + "| [2,4,17,26,34,58,129,205,329,593,770,987,1155,1239,1324,1204,1051,941,816... |\n", + "| [2,6,18,34,45,72,136,232,356,629,774,1005,1168,1211,1307,1178,1036,922,80... |\n", + "| [2,9,23,48,90,140,242,440,710,949,1221,1301,1371,1371,1249,981,819,673,51... |\n", + "| [2,9,23,48,90,140,242,440,710,949,1221,1301,1371,1371,1249,981,819,673,51... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------+----------------------+\n", + "| dp_hist_all.n_smaller | dp_hist_all.n_larger |\n", + "+-----------------------+----------------------+\n", + "| int64 | int64 |\n", + "+-----------------------+----------------------+\n", + "| 0 | 3596 |\n", + "| 0 | 4104 |\n", + "| 0 | 4057 |\n", + "| 0 | 3065 |\n", + "| 0 | 3065 |\n", + "+-----------------------+----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| ab_hist_alt.bin_edges |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------------------------------------------+-----------------------+\n", + "| ab_hist_alt.bin_freq | ab_hist_alt.n_smaller |\n", + "+-------------------------------------------+-----------------------+\n", + "| array | int64 |\n", + "+-------------------------------------------+-----------------------+\n", + "| [0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,6,5,6,5,4,2,1,0,1,1,0,0,0,0,0,0,0] | 0 |\n", + "| [0,3,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] | 0 |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] | 0 |\n", + "+-------------------------------------------+-----------------------+\n", + "\n", + "+----------------------+----------+-------------------+\n", + "| ab_hist_alt.n_larger | qual | vep.assembly_name |\n", + "+----------------------+----------+-------------------+\n", + "| int64 | float64 | str |\n", + "+----------------------+----------+-------------------+\n", + "| 0 | 3.04e+01 | \"GRCh37\" |\n", + "| 0 | 4.65e+04 | \"GRCh37\" |\n", + "| 0 | 8.98e+04 | \"GRCh37\" |\n", + "| 0 | 3.67e+04 | \"GRCh37\" |\n", + "| 0 | 3.67e+04 | \"GRCh37\" |\n", + "+----------------------+----------+-------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.allele_string |\n", + "+------------------------------------------------------------------------------+\n", + "| str |\n", + "+------------------------------------------------------------------------------+\n", + "| \"-/AACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\" |\n", + "| \"AACCCT/-\" |\n", + "| \"ACCCT/-\" |\n", + "| \"T/C\" |\n", + "| \"AACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACCCT... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+---------------+\n", + "| vep.ancestral |\n", + "+---------------+\n", + "| str |\n", + "+---------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.colocated_variants |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------+\n", + "| vep.most_severe_consequence |\n", + "+-----------------------------+\n", + "| str |\n", + "+-----------------------------+\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "+-----------------------------+\n", "\n", - "+----------+----------+\n", - "| over_50 | over_100 |\n", - "+----------+----------+\n", - "| float32 | float32 |\n", - "+----------+----------+\n", - "| 2.27e-03 | 0.00e+00 |\n", - "| 4.83e-03 | 2.79e-05 |\n", - "| 7.61e-03 | 5.58e-05 |\n", - "| 1.20e-02 | 1.67e-04 |\n", - "| 1.42e-02 | 2.37e-04 |\n", - "+----------+----------+\n", + "+------------------------------------------------------------------------------+\n", + "| vep.motif_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, high_inf_p... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| [(1,[\"TF_binding_site_variant\",\"TFBS_ablation\"],\"N\",\"MODERATE\",1,\"MA0341.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.regulatory_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [\"T\",\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\"] |\n", + "| [\"CAACCCT\",\"C\"] |\n", + "| [\"AACCCT\",\"A\"] |\n", + "| [\"T\",\"C\"] |\n", + "| [\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACC... |\n", + "+------------------------------------------------------------------------------+\n", "showing top 5 rows" ] }, @@ -6600,7 +14664,7 @@ "height": "calc(100% - 180px)", "left": "10px", "top": "150px", - "width": "202.438px" + "width": "220.42px" }, "toc_section_display": true, "toc_window_display": true diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb index c4b688a..c63ea3a 100644 --- a/gnomad_toolbox/notebooks/needs_a_name.ipynb +++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb @@ -43,7 +43,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -215,7 +215,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n", + " const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -321,7 +321,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -337,7 +337,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n \"
  • use INLINE resources instead, as so:
  • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n \"
  • use INLINE resources instead, as so:
  • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -348,7 +348,7 @@ "\n", "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n", "from gnomad_toolbox.filtering.variant import filter_by_intervals\n", - "from gnomad_toolbox.filtering.vep import filter_by_csqs\n", + "from gnomad_toolbox.filtering.vep import filter_by_consequence_category, filter_to_plofs\n", "from gnomad_toolbox.load_data import get_gnomad_release" ] }, @@ -497,7 +497,7 @@ "tags": [] }, "source": [ - "## Filter to pLOF variants that we used to compute constraint metrics\n", + "## Filter to pLoF variants that we used to compute constraint metrics\n", "pLOF variants meets the following requirements:\n", "* High-confidence LOFTEE variants (without any flags),\n", "* Only variants in the MANE Select transcript,\n", @@ -511,43 +511,187 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "6ce87a77", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'coverage' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# TODO: add function for this\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m coverage_ht \u001b[38;5;241m=\u001b[39m coverage(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexomes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mht()\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\u001b[39;00m\n\u001b[1;32m 6\u001b[0m ht \u001b[38;5;241m=\u001b[39m ht\u001b[38;5;241m.\u001b[39mfilter(\n\u001b[1;32m 7\u001b[0m (hl\u001b[38;5;241m.\u001b[39mlen(ht\u001b[38;5;241m.\u001b[39mfilters) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \n\u001b[1;32m 8\u001b[0m \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mallele_info\u001b[38;5;241m.\u001b[39mallele_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 9\u001b[0m \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mfreq[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mAF \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.001\u001b[39m)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;241m&\u001b[39m (coverage_ht[ht\u001b[38;5;241m.\u001b[39mlocus]\u001b[38;5;241m.\u001b[39mmedian_approx \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m 11\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'coverage' is not defined" + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO (gnomad.utils.vep 947): Filtering to canonical transcripts\n", + "INFO (gnomad.utils.vep 950): Filtering to MANE Select transcripts...\n", + "INFO (gnomad.utils.vep 953): Filtering to Ensembl transcripts...\n", + "INFO (gnomad.utils.vep 959): Filtering to genes of interest...\n", + "INFO (gnomad.utils.vep 967): Filtering to variants with additional criteria...\n" ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
" + ], + "text/plain": [ + "+----------------+------------+---------+----------+---------+\n", + "| locus | alleles | freq.AC | freq.AF | freq.AN |\n", + "+----------------+------------+---------+----------+---------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+---------+----------+---------+\n", + "| chr1:155337668 | [\"G\",\"A\"] | 459 | 3.14e-04 | 1461270 |\n", + "| chr1:155337704 | [\"G\",\"A\"] | 6 | 4.10e-06 | 1461714 |\n", + "| chr1:155337735 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461694 |\n", + "| chr1:155338087 | [\"A\",\"T\"] | 1 | 6.85e-07 | 1459648 |\n", + "| chr1:155338161 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461884 |\n", + "| chr1:155349380 | [\"T\",\"A\"] | 1 | 6.84e-07 | 1461768 |\n", + "| chr1:155354631 | [\"C\",\"T\"] | 1 | 6.88e-07 | 1453416 |\n", + "| chr1:155357583 | [\"A\",\"T\"] | 1 | 6.84e-07 | 1461680 |\n", + "| chr1:155370984 | [\"C\",\"T\"] | 3 | 2.06e-06 | 1455726 |\n", + "| chr1:155415924 | [\"C\",\"A\"] | 2 | 1.48e-06 | 1352032 |\n", + "| chr1:155478020 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461888 |\n", + "| chr1:155478203 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461874 |\n", + "| chr1:155478439 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461890 |\n", + "| chr1:155478528 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461876 |\n", + "| chr1:155479767 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461858 |\n", + "| chr1:155479862 | [\"G\",\"T\"] | 1 | 6.84e-07 | 1461160 |\n", + "| chr1:155521291 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461862 |\n", + "| chr1:155521474 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461724 |\n", + "+----------------+------------+---------+----------+---------+\n", + "\n", + "+-----------------------+-----------------------------+---------------+\n", + "| freq.homozygote_count | csq | coverage.mean |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| int64 | array | float64 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| 0 | [\"stop_gained\"] | 2.99e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.02e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.00e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.16e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.19e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.21e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.04e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.13e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.12e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 2.73e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.26e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.27e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.24e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.29e+01 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "\n", + "+------------------------+-------------------+-----------------+\n", + "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n", + "+------------------------+-------------------+-----------------+\n", + "| int32 | int64 | float64 |\n", + "+------------------------+-------------------+-----------------+\n", + "| 30 | 21887528 | 1.00e+00 |\n", + "| 30 | 22042372 | 1.00e+00 |\n", + "| 30 | 21956914 | 1.00e+00 |\n", + "| 31 | 23111319 | 1.00e+00 |\n", + "| 31 | 23308434 | 1.00e+00 |\n", + "| 32 | 23444999 | 1.00e+00 |\n", + "| 31 | 22209714 | 1.00e+00 |\n", + "| 31 | 22858887 | 1.00e+00 |\n", + "| 31 | 22802181 | 1.00e+00 |\n", + "| 30 | 19932375 | 1.00e+00 |\n", + "| 33 | 23830081 | 1.00e+00 |\n", + "| 33 | 23924871 | 1.00e+00 |\n", + "| 33 | 23994800 | 1.00e+00 |\n", + "| 33 | 23997776 | 1.00e+00 |\n", + "| 33 | 24143459 | 1.00e+00 |\n", + "| 32 | 23651203 | 1.00e+00 |\n", + "| 32 | 24114456 | 1.00e+00 |\n", + "| 32 | 24077551 | 1.00e+00 |\n", + "+------------------------+-------------------+-----------------+\n", + "\n", + "+-----------------+------------------+------------------+------------------+\n", + "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.86e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.94e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.92e-01 |\n", + "| 1.00e+00 | 9.99e-01 | 9.95e-01 | 9.89e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 |\n", + "| 1.00e+00 | 9.95e-01 | 9.83e-01 | 9.70e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 9.84e-01 | 9.41e-01 | 8.72e-01 | 8.17e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.91e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "\n", + "+------------------+------------------+------------------+-------------------+\n", + "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| 9.32e-01 | 6.07e-01 | 8.96e-04 | 6.02e-05 |\n", + "| 9.48e-01 | 6.21e-01 | 8.39e-04 | 4.93e-05 |\n", + "| 9.37e-01 | 6.12e-01 | 8.29e-04 | 4.51e-05 |\n", + "| 9.59e-01 | 7.28e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.42e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.69e-01 | 5.01e-03 | 1.85e-04 |\n", + "| 9.28e-01 | 6.53e-01 | 4.38e-04 | 4.93e-05 |\n", + "| 9.79e-01 | 7.47e-01 | 8.14e-04 | 6.29e-05 |\n", + "| 9.54e-01 | 6.97e-01 | 1.14e-03 | 3.69e-05 |\n", + "| 7.64e-01 | 5.70e-01 | 1.35e-03 | 1.76e-04 |\n", + "| 9.72e-01 | 7.53e-01 | 3.11e-03 | 1.40e-03 |\n", + "| 9.72e-01 | 7.55e-01 | 4.07e-03 | 2.20e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.64e-03 | 2.58e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.67e-03 | 2.60e-03 |\n", + "| 9.74e-01 | 7.66e-01 | 6.55e-03 | 3.89e-03 |\n", + "| 9.42e-01 | 7.13e-01 | 6.42e-03 | 3.91e-03 |\n", + "| 9.81e-01 | 7.72e-01 | 1.94e-02 | 3.68e-03 |\n", + "| 9.79e-01 | 7.69e-01 | 1.94e-02 | 3.67e-03 |\n", + "+------------------+------------------+------------------+-------------------+" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# TODO: add function for this\n", - "\n", - "coverage_ht = coverage(\"exomes\").ht()\n", - "\n", - "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n", - "ht = ht.filter(\n", - " (hl.len(ht.filters) == 0) \n", - " & (ht.allele_info.allele_type == \"snv\")\n", - " & (ht.freq[0].AF <= 0.001)\n", - " & (coverage_ht[ht.locus].median_approx >= 30)\n", - ")\n", - "\n", - "\n", - "print(f\"Number of variants: {ht.count()}\")\n", - "ht.select(\n", - " freq=ht.freq[0],\n", - " csq=ht.vep.transcript_consequences[0].consequence_terms,\n", - " coverage=coverage_ht[ht.locus],\n", - ").show(-1)" + "filter_to_plofs('ash1l', select_fields=True).show(-1)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c2212f4-a7cf-45bc-930d-aa18cebff7a3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -566,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.11.9" }, "toc": { "base_numbering": 1, diff --git a/requirements.txt b/requirements.txt index 702bdab..b0183e0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,4 @@ -# We're using the main branch of gnomad_method on github rather than the pip version -# TODO: Decide on how to handle this. We might just need to have more releases of gnomad_methods. -git+https://github.com/broadinstitute/gnomad_methods@main +gnomad @ git+https://github.com/broadinstitute/gnomad_methods@main hail jupyter jupyter_contrib_nbextensions