期刊
GENOME BIOLOGY
卷 21, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13059-020-1949-z
关键词
scRNA-seq; Functional analysis; Transcription factor analysis; Pathway analysis; Benchmark
资金
- German Federal Ministry of Education and Research (BMBF) [FKZ: 031 L0049]
- NIH [U54-CA217377]
- Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences
- Spanish Institute of Health Carlos III (ISCIII) [CP14/00229]
- Ministerio de Ciencia, Innovacion y Universidades (AEI/FEDER, UE) [SAF2017-89109-P]
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.
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