Revert "Squashed commit of the following:"

This reverts commit 1d0bd6d.

.github/workflows/test-runner.yml

@@ -42,24 +42,3 @@ jobs:
       - name: Run pytest preprocessing
         run: pytest -v ${{ github.workspace }}/tests/preprocessing
         shell: micromamba-shell {0}
-
-  DeepRVAT-Tests-Runner-Annotations:
-    runs-on: ubuntu-latest
-    steps:
-
-      - name: Check out repository code
-        uses: actions/checkout@v4
-      - uses: mamba-org/setup-micromamba@v1.8.0
-        with:
-          environment-name: deeprvat-annotation-gh-action
-          environment-file: ${{ github.workspace }}/deeprvat_annotations.yml
-          cache-environment: true
-          cache-downloads: true
-
-      - name: Install DeepRVAT
-        run: pip install -e ${{ github.workspace }}
-        shell: micromamba-shell {0}
-
-      - name: Run pytest annotations
-        run: pytest -v ${{ github.workspace }}/tests/annotations
-        shell: micromamba-shell {0}

deeprvat/annotations/annotations.py

@@ -1,7 +1,5 @@
 import logging
 import os
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
 import pickle
 import random
 import sys
@@ -1082,7 +1080,7 @@ def aggregate_abscores(
         delayed(process_chunk)(
             i, abs_splice_res_dir, tissues_to_exclude, tissue_agg_function, ca_shortened
         )
-        for i in tqdm(sorted(os.listdir(abs_splice_res_dir)))
+        for i in tqdm(os.listdir(abs_splice_res_dir))
     )
     all_absplice_scores = list(output_generator)
 
@@ -1362,6 +1360,45 @@ def merge_deepsea_pcas(
     merged.to_parquet(out_file)
 
 
+@cli.command()
+@click.argument("in_variants", type=click.Path(exists=True))
+@click.argument("out_variants", type=click.Path())
+def process_annotations(in_variants: str, out_variants: str):
+    """
+    Process variant annotations, filter for canonical variants, and aggregate consequences.
+
+    Parameters:
+    - in_variants (str): Path to the input variant annotation file in parquet format.
+    - out_variants (str): Path to save the processed variant annotation file in parquet format.
+
+    Returns:
+    None
+
+    Notes:
+    - The function reads the input variant annotation file.
+    - It filters for canonical variants where the 'CANONICAL' column is equal to 'YES'.
+    - The 'Gene' column is renamed to 'gene_id'.
+    - Consequences for different alleles are aggregated by combining the variant ID with the gene ID.
+    - The processed variant annotations are saved to the specified output file.
+
+    Example:
+    $ python annotations.py process_annotations input_variants.parquet output_variants.parquet
+    """
+    variant_path = Path(in_variants)
+    variants = pd.read_parquet(variant_path)
+
+    logger.info("filtering for canonical variants")
+
+    variants = variants.loc[variants.CANONICAL == "YES"]
+    variants.rename(columns={"Gene": "gene_id"}, inplace=True)
+
+    logger.info("aggregating consequences for different alleles")
+
+    # combining variant id with gene id
+    variants["censequence_id"] = variants["id"].astype(str) + variants["gene_id"]
+    variants.to_parquet(out_variants, compression="zstd")
+
+
 def process_chunk_addids(chunk: pd.DataFrame, variants: pd.DataFrame) -> pd.DataFrame:
     """
     Process a chunk of data by adding identifiers from a variants dataframe.
@@ -1470,14 +1507,16 @@ def add_ids(annotation_file: str, variant_file: str, njobs: int, out_file: str):
 @cli.command()
 @click.argument("annotation_file", type=click.Path(exists=True))
 @click.argument("variant_file", type=click.Path(exists=True))
+@click.argument("njobs", type=int)
 @click.argument("out_file", type=click.Path())
-def add_ids_dask(annotation_file: str, variant_file: str, out_file: str):
+def add_ids_dask(annotation_file: str, variant_file: str, njobs: int, out_file: str):
     """
     Add identifiers from a variant file to an annotation file using Dask and save the result.
 
     Parameters:
     - annotation_file (str): Path to the input annotation file in Parquet format.
     - variant_file (str): Path to the input variant file in Parquet format.
+    - njobs (int): Number of parallel jobs to process the data.
     - out_file (str): Path to save the processed data in Parquet format.
 
     Returns:
@@ -1493,7 +1532,7 @@ def add_ids_dask(annotation_file: str, variant_file: str, out_file: str):
     $ python annotations.py add_ids_dask annotation_data.parquet variant_data.parquet 4 processed_data.parquet
     """
     data = dd.read_parquet(annotation_file, blocksize=25e9)
-    all_variants = pd.read_parquet(variant_file)
+    all_variants = pd.read_table(variant_file)
     data = data.rename(
         columns={
             "#CHROM": "chrom",
@@ -1666,7 +1705,7 @@ def merge_annotations(
     logger.info("load variant_file")
 
     logger.info(f"reading in {variant_file}")
-    variants = pd.read_parquet(variant_file)
+    variants = pd.read_csv(variant_file, sep="\t")
 
     logger.info("merge vep to variants M:1")
     ca = vep_df.merge(
@@ -1738,23 +1777,7 @@ def process_vep(
         vcf_file, names=["chrom", "pos", "#Uploaded_variation", "ref", "alt"]
     )
     if "#Uploaded_variation" in vep_file.columns:
-        vep_file = vep_file.merge(vcf_df, on="#Uploaded_variation", how="left")
-        if vep_file.chrom.isna().sum() > 0:
-            vep_file.loc[vep_file.chrom.isna(), ["chrom", "pos", "ref", "alt"]] = (
-                vep_file[vep_file["chrom"].isna()]["#Uploaded_variation"]
-                .str.replace("_", ":")
-                .str.replace("/", ":")
-                .str.split(":", expand=True)
-                .values
-            )
-    assert vep_file.chrom.isna().sum() == 0
-    assert vep_file.pos.isna().sum() == 0
-    assert vep_file.ref.isna().sum() == 0
-    assert vep_file.alt.isna().sum() == 0
-    assert (
-        vep_file[["chrom", "pos", "ref", "alt"]].drop_duplicates().shape
-        == vcf_df[["chrom", "pos", "ref", "alt"]].drop_duplicates().shape
-    )
+        vep_file = vep_file.merge(vcf_df, on="#Uploaded_variation")
 
     if "pos" in vep_file.columns:
         vep_file["pos"] = vep_file["pos"].astype(int)
@@ -1956,7 +1979,7 @@ def get_af_from_gt(genotype_file: str, variants_filepath: str, out_file: str):
     """
     import h5py
 
-    variants = pd.read_parquet(variants_filepath)
+    variants = pd.read_table(variants_filepath)
     max_variant_id = variants["id"].max()
 
     logger.info("Computing allele frequencies")
@@ -2019,19 +2042,19 @@ def calculate_maf(annotations_path: str, out_file: str):
 
 
 @cli.command()
-@click.argument("gene_id_file", type=click.Path(exists=True))
+@click.argument("protein_id_file", type=click.Path(exists=True))
 @click.argument("annotations_path", type=click.Path(exists=True))
 @click.argument("out_file", type=click.Path())
-def add_gene_ids(gene_id_file: str, annotations_path: str, out_file: str):
+def add_protein_ids(protein_id_file: str, annotations_path: str, out_file: str):
     """
-    Add gene IDs to the annotations based on gene ID mapping file.
+    Add protein IDs to the annotations based on protein ID mapping file.
 
     Parameters:
-    - gene_id_file (str): Path to the gene ID mapping file.
+    - protein_id_file (str): Path to the protein ID mapping file.
     - annotations_path (str): Path to the annotations file.
     - out_file (str): Path to the output file to save the annotations with protein IDs.
     """
-    genes = pd.read_parquet(gene_id_file)
+    genes = pd.read_parquet(protein_id_file)
     genes[["gene_base", "feature"]] = genes["gene"].str.split(".", expand=True)
     genes.drop(columns=["feature", "gene", "gene_name", "gene_type"], inplace=True)
     genes.rename(columns={"id": "gene_id"}, inplace=True)
@@ -2046,7 +2069,7 @@ def add_gene_ids(gene_id_file: str, annotations_path: str, out_file: str):
 @cli.command()
 @click.argument("gtf_filepath", type=click.Path(exists=True))
 @click.argument("out_file", type=click.Path())
-def create_gene_id_file(gtf_filepath: str, out_file: str):
+def create_protein_id_file(gtf_filepath: str, out_file: str):
     """
     Create a protein ID mapping file from the GTF file.
 

deeprvat/cv_utils.py

@@ -59,11 +59,9 @@ def spread_config(
     cv_path = f"{config_template['cv_path']}/{n_folds}_fold"
     for module in data_modules:
         config = copy.deepcopy(config_template)
-        data_slots = DATA_SLOT_DICT[module]
-        for data_slot in data_slots:
-            sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
-            logger.info(f"setting sample file {sample_file}")
-            config[data_slot]["dataset_config"]["sample_file"] = sample_file
+        sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
+        logger.info(f"setting sample file {sample_file}")
+        config["sample_file"] = sample_file
 
         if (module == "deeprvat") | (module == "deeprvat_pretrained"):
             logger.info("Writing baseline directories")
@@ -91,8 +89,7 @@ def generate_test_config(input_config, out_file, fold, n_folds):
     split = "test"
     sample_file = f"{cv_path}/samples_{split}{fold}.pkl"
     logger.info(f"setting sample file {sample_file}")
-    for data_slot in DATA_SLOT_DICT["deeprvat"]:
-        config[data_slot]["dataset_config"]["sample_file"] = sample_file
+    config["sample_file"] = sample_file
     with open(out_file, "w") as f:
         yaml.dump(config, f)
 

deeprvat/data/dense_gt.py

@@ -362,7 +362,7 @@ def setup_phenotypes(
         # account for the fact that genotypes.h5 and phenotype_df can have different
         # orders of their samples
         self.index_map_geno, _ = get_matched_sample_indices(
-            samples_gt.astype(int), self.samples.astype(int)
+            samples_gt.astype(str), self.samples.astype(str)
         )
         # get_matched_sample_indices is a much, much faster implementation of the code below
         # self.index_map_geno = [np.where(samples_gt.astype(int) == i) for i in self.samples.astype(int)]
@@ -614,13 +614,20 @@ def setup_variants(
                     "Annotation dataframe has inconsistent allele frequency values"
                 )
             variants_with_af = safe_merge(
-                variants[["id"]].reset_index(drop=True), af_annotation
+                variants[["id"]].reset_index(drop=True), af_annotation, how="left"
             )
             assert np.all(
                 variants_with_af["id"].to_numpy() == variants["id"].to_numpy()
             )
-            mask = (variants_with_af[af_col] >= af_threshold) & (
-                variants_with_af[af_col] <= 1 - af_threshold
+            af_isna = variants_with_af[af_col].isna()
+            if af_isna.sum() > 0:
+                logger.warning(
+                    f"Dropping {af_isna.sum()} variants missing from annotation dataframe"
+                )
+            mask = (
+                (~af_isna)
+                & (variants_with_af[af_col] >= af_threshold)
+                & (variants_with_af[af_col] <= 1 - af_threshold)
             )
             mask = mask.to_numpy()
             del variants_with_af
@@ -931,11 +938,10 @@ def get_metadata(self) -> Dict[str, Any]:
         result = {
             "variant_metadata": self.variants[
                 ["id", "common_variant_mask", "rare_variant_mask", "matrix_index"]
-            ]
+            ],
+            "samples": self.samples,
         }
         if self.use_rare_variants:
             if hasattr(self.rare_embedding, "get_metadata"):
-                result.update(
-                    {"rare_embedding_metadata": self.rare_embedding.get_metadata()}
-                )
+                result["rare_embedding_metadata"] = self.rare_embedding.get_metadata()
         return result

deeprvat/deeprvat/associate.py

@@ -7,7 +7,7 @@
 import sys
 from pathlib import Path
 from pprint import pprint
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import click
 import dask.dataframe as dd
@@ -115,9 +115,7 @@ def cli():
 
 def make_dataset_(
     config: Dict,
-    debug: bool = False,
     data_key="data",
-    samples: Optional[List[int]] = None,
 ) -> Dataset:
     """
     Create a dataset based on the configuration.
@@ -149,29 +147,17 @@ def make_dataset_(
             **copy.deepcopy(data_config["dataset_config"]),
         )
 
-        restrict_samples = config.get("restrict_samples", None)
-        if debug:
-            logger.info("Debug flag set; Using only 1000 samples")
-            ds = Subset(ds, range(1_000))
-        elif samples is not None:
-            ds = Subset(ds, samples)
-        elif restrict_samples is not None:
-            ds = Subset(ds, range(restrict_samples))
-
     return ds
 
 
 @cli.command()
-@click.option("--debug", is_flag=True)
 @click.option("--data-key", type=str, default="data")
-@click.argument("config-file", type=click.Path(exists=True))
-@click.argument("out-file", type=click.Path())
-def make_dataset(debug: bool, data_key: str, config_file: str, out_file: str):
+@click.argument("config-file", type=click.Path(exists=True, path_type=Path))
+@click.argument("out-file", type=click.Path(path_type=Path))
+def make_dataset(data_key: str, config_file: Path, out_file: Path):
     """
     Create a dataset based on the provided configuration and save to a pickle file.
 
-    :param debug: Flag for debugging.
-    :type debug: bool
     :param data_key: Key for dataset configuration in the config dictionary, defaults to "data".
     :type data_key: str
     :param config_file: Path to the configuration file.
@@ -183,7 +169,7 @@ def make_dataset(debug: bool, data_key: str, config_file: str, out_file: str):
     with open(config_file) as f:
         config = yaml.safe_load(f)
 
-    ds = make_dataset_(config, debug=debug, data_key=data_key)
+    ds = make_dataset_(config, data_key=data_key)
 
     with open(out_file, "wb") as f:
         pickle.dump(ds, f)
@@ -236,6 +222,8 @@ def compute_burdens_(
     .. note::
         Checkpoint models all corresponding to the same repeat are averaged for that repeat.
     """
+    logger.setLevel(logging.INFO)
+
     if not skip_burdens:
         logger.info("agg_models[*][*].reverse:")
         pprint(
@@ -247,10 +235,38 @@ def compute_burdens_(
 
     data_config = config["data"]
 
-    ds_full = ds.dataset if isinstance(ds, Subset) else ds
+    if "sample_file" in config:
+        sample_file = Path(config["sample_file"])
+        logger.info(f"Using samples from {sample_file}")
+        if sample_file.suffix == ".pkl":
+            with open(sample_file, "rb") as f:
+                sample_ids = np.array(pickle.load(f))
+        elif sample_file.suffix == ".zarr":
+            sample_ids = zarr.load(sample_file)
+        elif sample_file.suffix == ".npy":
+            sample_ids = np.load(sample_file)
+        else:
+            raise ValueError("Unknown file type for sample_file")
+        ds_samples = ds.get_metadata()["samples"]
+        sample_indices = np.where(
+            np.isin(ds_samples.astype(str), sample_ids.astype(str))
+        )[0]
+        if debug:
+            sample_indices = sample_indices[:1000]
+    elif debug:
+        sample_indices = np.arange(min(1000, len(ds)))
+    else:
+        sample_indices = np.arange(len(ds))
+
+    logger.info(
+        f"Computing gene impairment for {sample_indices.shape[0]} samples: {sample_indices}"
+    )
+    ds = Subset(ds, sample_indices)
+
+    ds_full = ds.dataset  # if isinstance(ds, Subset) else ds
     collate_fn = getattr(ds_full, "collate_fn", None)
     n_total_samples = len(ds)
-    ds.rare_embedding.skip_embedding = skip_burdens
+    ds_full.rare_embedding.skip_embedding = skip_burdens
 
     if chunk is not None:
         if n_chunks is None:
@@ -903,7 +919,7 @@ def compute_burdens(
         with open(dataset_file, "rb") as f:
             dataset = pickle.load(f)
     else:
-        dataset = make_dataset_(config)
+        dataset = make_dataset_(data_config)
 
     if torch.cuda.is_available():
         logger.info("Using GPU")