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

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 knockout. 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.

Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

“…The number of cells per amplification pool depends on the amount of amplifiable cDNA, implying that the good performance of Quartz-Seq2 was mainly due to efficient conversion of amplifiable cDNA from RNA with poly(A) tagging. It is equally important to benchmark computational pipelines for data analysis and interpretation 23,[42][43][44] . We envision the datasets provided by our study serving as a valuable resource for the single-cell community to develop and evaluate new strategies for an informative and interpretable cell atlas.…”

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

“…Encouragingly, the footprint methods that bring data into COSMOS seem fairly robust to the characteristics of single-cell RNA data such as dropouts 49 . Finally, we expect that in the future data generation technologies will increase coverage and our prior knowledge will become more complete, reducing the mentioned limitations.…”

Single cell RNA sequencing of blood antigen-presenting cells in severe Covid-19 reveals multi-process defects in antiviral immunity

“…In order to characterize molecular pathways in AS-DC sub-clusters, we used two complementary approaches. First, we inferred transcription factor (TF) activity using Dorothea algorithm 26 and scored the activity of each regulon using Viper inference tool 27 .…”