December 10, 2019 Dataset Open Access

Christian H. Holland; Jovan Tanevski; Javier Perales-Patón; Jan Gleixner; Manu P. Kumar; Elisabetta Mereu; Brian A. Joughin; Oliver Stegle; Douglas A. Lauffenburger; Holger Heyn; Bence Szalai; Julio Saez-Rodriguez

JSON-LD (schema.org) Export

{
  "description": "<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href=\"https://doi.org/10.1186/s13059-020-1949-z\">Holland et al. 2020</a>.</p>\n\n<p>The folder&nbsp;<em>data </em>contains<em>&nbsp;</em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses.&nbsp;</p>\n\n<p>The associated analyses code and more information are available on&nbsp;<a href=\"https://github.com/saezlab/FootprintMethods_on_scRNAseq\">GitHub</a>.</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Abstract</strong></p>\n\n<p><strong>Background</strong></p>\n\n<p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p>\n\n<p><strong>Results</strong></p>\n\n<p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p>\n\n<p><strong>Conclusions</strong></p>\n\n<p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p>\n\n<p>&nbsp;</p>\n\n<p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href=\"https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues\">GitHub issue system</a>.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant - Im Neuenheimer Feld 267, 69120 Heidelberg, Germany", 
      "@id": "https://orcid.org/0000-0002-3060-5786", 
      "@type": "Person", 
      "name": "Christian H. Holland"
    }, 
    {
      "affiliation": "Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant - Im Neuenheimer Feld 267, 69120 Heidelberg, Germany", 
      "@type": "Person", 
      "name": "Jovan Tanevski"
    }, 
    {
      "affiliation": "Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant - Im Neuenheimer Feld 267, 69120 Heidelberg, Germany", 
      "@type": "Person", 
      "name": "Javier Perales-Pat\u00f3n"
    }, 
    {
      "affiliation": "German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany", 
      "@type": "Person", 
      "name": "Jan Gleixner"
    }, 
    {
      "affiliation": "Department of Biological Engineering, MIT, Cambridge MA", 
      "@type": "Person", 
      "name": "Manu P. Kumar"
    }, 
    {
      "affiliation": "CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain", 
      "@type": "Person", 
      "name": "Elisabetta Mereu"
    }, 
    {
      "affiliation": "Department of Biological Engineering, MIT, Cambridge MA", 
      "@type": "Person", 
      "name": "Brian A. Joughin"
    }, 
    {
      "affiliation": "German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany", 
      "@type": "Person", 
      "name": "Oliver Stegle"
    }, 
    {
      "affiliation": "Department of Biological Engineering, MIT, Cambridge MA", 
      "@type": "Person", 
      "name": "Douglas A. Lauffenburger"
    }, 
    {
      "affiliation": "CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain", 
      "@type": "Person", 
      "name": "Holger Heyn"
    }, 
    {
      "affiliation": "Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary", 
      "@type": "Person", 
      "name": "Bence Szalai"
    }, 
    {
      "affiliation": "Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant - Im Neuenheimer Feld 267, 69120 Heidelberg, Germany", 
      "@id": "https://orcid.org/0000-0002-8552-8976", 
      "@type": "Person", 
      "name": "Julio Saez-Rodriguez"
    }
  ], 
  "url": "https://zenodo.org/record/3564179", 
  "datePublished": "2019-12-10", 
  "version": "Version 2019-12-10", 
  "keywords": [
    "scRNA-seq", 
    "functional analysis", 
    "transcription factor analysis", 
    "pathway analysis", 
    "benchmark"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/dc049add-b231-4dd6-ba3d-a5f69ce57c11/data.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/dc049add-b231-4dd6-ba3d-a5f69ce57c11/output.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.3564179", 
  "@id": "https://doi.org/10.5281/zenodo.3564179", 
  "@type": "Dataset", 
  "name": "Robustness and applicability of  transcription factor and pathway analysis tools on single-cell RNA-seq data"
}