Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
DOI10.5281/zenodo.3564179Zenodo3564179MaRDI QIDQ6692105
Dataset published at Zenodo repository.
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Publication date: 10 December 2019
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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 folderdata containsraw data and the folder output contains intermediate and final results of all analyses. The associated analyses code and more information are available onGitHub. 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 christian.holland@bioquant.uni-heidelberg.de or use the GitHub issue system.
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