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32051003
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.

Genome Biol, 2020/02/12;21(1):36.

Holland CH[1, 2], Tanevski J[1, 3], Perales-Patón J[1], Gleixner J[4, 5], Kumar MP[6], Mereu E[7], Joughin BA[6, 8], Stegle O[4, 5, 9], Lauffenburger DA[6], Heyn H[7, 10], Szalai B[11], Saez-Rodriguez J[12, 13]

Affiliations

PMID: 32051003DOI: 10.1186/s13059-020-1949-z

Impact factor: 17.906

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.

Keywords: Benchmark; Functional analysis; Pathway analysis; Transcription factor analysis; scRNA-seq

MeSH terms
Animals; Benchmarking; Gene Regulatory Networks; Humans; RNA-Seq; Single-Cell Analysis; Software; Transcription Factors; Transcriptome
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