DESCRIPTION

Package: stream
Title: Enhancer-driven gene regulatory network inference from single-cell RNA-seq and ATAC-seq 
Version: 2.0.0
Date: 2023-1-27
Authors@R: 
  c(person(given = "Yang", 
          family = "Li", 
          email = "liyang.bioinformatics@gmail.com", 
          role = c("aut", "cre"), 
          comment = c(ORCID = "0000-0002-7677-9028")),
    person(given = "Anjun", 
          family = "Ma", 
          email = "Anjun.Ma@osumc.edu", 
          role = "aut", 
          comment = c(ORCID = "0000-0001-6269-398X")), 
    person(given = "Yizhong", 
           family = "Wang", 
           email = "201911843@mail.sdu.edu.cn", 
           role = "aut"), 
    person(given = "Qi", 
           family = "Guo", 
           email = "Qi.Guo@osumc.edu", 
           role = "aut"), 
    person(given = "Cankun", 
           family = "Wang", 
           email = "Cankun.Wang@osumc.edu", 
           role = "aut", 
           comment = c(ORCID = "0000-0002-0225-9855")), 
    person(given = "Bingqiang", 
           family = "Liu", 
           email = "bingqiang@sdu.edu.cn", 
           role = "ctb", 
           comment = c(ORCID = "0000-0002-5734-1135")), 
    person(given = "Qin", 
           family = "Ma", 
           email = "Qin.Ma@osumc.edu", 
           role = "ctb",
           comment = c(ORCID = "0000-0002-3264-8392")))
Description: We present a new algorithm, STREAM, for enhancer-driven gene regulatory network (eGRN) inference 
    from transcriptome and chromatin accessibility profiled from the same single cell populations. The algorithm 
    substantially improves the prediction accuracy of relations among transcription factors (TFs), enhancers, and 
    genes, by achieving global optimization based on two key new ideas: (i) we developed the Steiner forest problem
    (SFP) model based on a heterogeneous graph to identify the set of highly confident enhancer-gene relations which
    underlie a context-specific functional gene module (FGM); and (ii) we designed a hybrid biclustering pipeline
    integrated with submodular optimization for inferring eGRNs by identifying the optimal subset from a set of 
    hybrid biclusters (HBCs), each of which represents co-regulated genes by the same TF and, co-accessible 
    enhancers bound by the same TF, occurring over a subset of cells. These two key ideas are embedded in an iterative
    framework for eGRN inference through by finding patterns in a pair of transcriptome and chromatin accessibility
    matrices. Benchmarking analysis shows that the performance, assessed by f-scores, precision, or recall, was
    significantly improved by our program compared to four other state-of-the-art tools on ten single-cell sequencing
    datasets from seven sequencing techniques. The applicative powerbility of STREAM was demonstrated through two
    biological case studies. By implementing STREAM on an Alzheimer’s disease dataset over a time coursediffuse small
    lymphocytic lymphoma dataset, we showcased its capability to identify TF-enhancer-gene relations associated with
    pseudotime and investigate the changing of enhancer-gene alongside cell lineagesexcavated the key TF-enhancer-gene
    relations and cooperation among TFs underlying diseased cell types. Additionally, by implementing STREAM on a
    diffuse small lymphocytic lymphoma dataset, we excavated the key TF-enhancer-gene relations and cooperation among
    TFs underlying diseased cell types.STREAM showcased its capability to identify TF-enhancer-gene relations which
    were associated with pseudotime and investigate the changing of enhancer-gene alongside cell lineages from an
    Alzheimer’s disease dataset over a time course. 
Reference: Li et al. (2022) <doi:10.1101/2022.12.15.520582>.
Depends:
    R (>= 4.2.1),
    Biobase
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
LazyData: false
RoxygenNote: 7.2.3
URL: https://github.com/YangLi-Bio/stream2
biocViews: 
    Software,
    SingleCell,
    RNASeq,
    ATACSeq,
    Normalization,
    Preprocessing,
    DimensionReduction,
    Visualization,
    QualityControl,
    BiClustering,
    GeneExpression,
    DifferentialExpression,
    ChromatinAccessibility,
    DifferentialAccessibility,
    FunctionalGeneModules,
    Regulon,
    Network
Imports:
    AnnotationDbi,
    BiocGenerics,
    BSgenome,
    BSgenome.Hsapiens.UCSC.hg19,
    BSgenome.Hsapiens.UCSC.hg38,
    BSgenome.Mmusculus.UCSC.mm10,
    biomaRt,
    data.table,
    dorothea,
    dplyr,
    EnsDb.Mmusculus.v79,
    EnsDb.Hsapiens.v75,
    EnsDb.Hsapiens.v86,
    easypackages,
    enrichR,
    ensembldb,
    GenomeInfoDb,
    GenomicAlignments,
    GenomicRanges,
    ggplot2,
    gUtils,
    IRanges,
    IRISFGM,
    igraph, 
    JASPAR2022,
    knitr,
    Matrix,
    monocle3,
    motifmatchr,
    parallel,
    pbapply,
    pbmcapply,
    ppcor,
    qgraph,
    qualV,
    RColorBrewer,
    RCurl,
    Repitools,
    Ryacas,
    regioneR,
    rTRM,
    S4Vectors,
    SingleCellExperiment,
    scales,
    Seurat,
    Signac,
    simpIntLists,
    STRINGdb,
    stats,
    SummarizedExperiment,
    TFBSTools,
    utils
Collate: 
    'cistrome_tools.R'
    'epigenome_tools.R'
    'multiome_tools.R'
    'protein.R'
    'reguome_tools.R'
    'run_stream.R'
    'stream2.R'
    'transcriptome_tools.R'
    'utilities.R'
    'visual.R'
Suggests: 
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2023-01-27 16:17:53 UTC; brent
Maintainer: Yang Li <liyang.bioinformatics@gmail.com>