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.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.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.
