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

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

doi:http://dx.doi.org/10.1186/s13059-020-1949-z

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

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Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 2020 Feb 12; 21(1): 36. DOI: 10.1186/s13059-020-1949-z.

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.