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

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    <subfield code="u">Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant - Im Neuenheimer Feld 267, 69120 Heidelberg, Germany</subfield>
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    <subfield code="a">Christian H. Holland</subfield>
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    <subfield code="a">Robustness and applicability of  transcription factor and pathway analysis tools on single-cell RNA-seq data</subfield>
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    <subfield code="a">&lt;p&gt;Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in &lt;a href="https://doi.org/10.1186/s13059-020-1949-z"&gt;Holland et al. 2020&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The folder&amp;nbsp;&lt;em&gt;data &lt;/em&gt;contains&lt;em&gt;&amp;nbsp;&lt;/em&gt;raw data and the folder &lt;em&gt;output&lt;/em&gt; contains intermediate and final results of all analyses.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The associated analyses code and more information are available on&amp;nbsp;&lt;a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq"&gt;GitHub&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Conclusions&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the &lt;a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues"&gt;GitHub issue system&lt;/a&gt;.&lt;/p&gt;</subfield>
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