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We began analyzing https://zenodo.org/records/3564179, but it redirected us to https://zenodo.org/records/3564179. The analysis below is for the second page.

Title[redir]:
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
Description:
Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in Holland et al. 2020. The folder data contains raw data and the folder output contains intermediate and final results of all analyses. The associated analyses code and more information are available on GitHub. Abstract Background 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. Results 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. Conclusions 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. For questions related to the data please write an email to [email protected] or use the GitHub issue system.

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Keywords {🔍}

data, analysis, tools, scrnaseq, pathway, functional, transcription, factor, singlecell, show, bulk, cookies, version, dataset, rnaseq, analyses, github, benchmark, simulated, real, download, zenodo, perform, december, robustness, applicability, holland, folder, results, sequencing, single, cells, applied, activities, performance, gene, sets, find, files, datazip, preview, citations, details, export, blog, support, policy, accept, skip, main,

Topics {✒️}

single-cell rna-seq data pathway analysis tools functional analysis tools real scrna-seq data single-cell rna sequencing bulk-rna tools progeny 00 utc zenodo transcription factor scrna-seq data perform benchmark studies 13 scrna-seq protocols benchmark data github issue system bulk transcriptome data original bulk data scrna-seq simulate single cells tools scenic/aucell dataset analysis functional tf storage cluster upgrade low library sizes estimate pathway crispr-mediated knock mixture sample sequenced published december 10 extract functional single cells bulk sequencing gene sets support folder data raw data files data show github simulated data folder output main communities dashboard log planned intervention july 16th 05 abstract background mechanistic insight activities silico study questions related christian uni-heidelberg

Schema {🗺️}

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 dateCreated:2019-12-10T16:39:03.831386+00:00
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 datePublished:2019-12-10
 description: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>. The folder data contains raw data and the folder output contains intermediate and final results of all analyses.  The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.   Abstract Background 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. Results 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. Conclusions 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.   For questions related to the data please write an email to [email protected] or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.
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