dc.contributor.author |
Holland, Christian H |
|
dc.contributor.author |
Tanevski, Jovan |
|
dc.contributor.author |
Perales-Patón, Javier |
|
dc.contributor.author |
Gleixner, Jan |
|
dc.contributor.author |
Kumar, Manu P |
|
dc.contributor.author |
Mereu, Elisabetta |
|
dc.contributor.author |
Joughin, Brian A |
|
dc.contributor.author |
Stegle, Oliver |
|
dc.contributor.author |
Lauffenburger, Douglas A |
|
dc.contributor.author |
Heyn, Holger |
|
dc.contributor.author |
Szalai, Bence |
|
dc.contributor.author |
Saez-Rodriguez, Julio |
|
dc.date.accessioned |
2020-04-09T07:09:36Z |
|
dc.date.available |
2020-04-09T07:09:36Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
journalVolume=21;journalIssueNumber=1;journalTitle=GENOME BIOLOGY;pagination=36, pages: 19;journalAbbreviatedTitle=GENOME BIOL; |
|
dc.identifier.uri |
http://repo.lib.semmelweis.hu//handle/123456789/8236 |
|
dc.identifier.uri |
doi:10.1186/s13059-020-1949-z |
|
dc.description.abstract |
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. |
|
dc.relation.ispartof |
urn:issn:1474-7596 |
|
dc.title |
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
|
dc.type |
Journal Article |
|
dc.date.updated |
2020-03-23T08:51:46Z |
|
dc.language.rfc3066 |
en |
|
dc.rights.holder |
NULL |
|
dc.identifier.mtmt |
31257077 |
|
dc.identifier.wos |
000514665200003 |
|
dc.identifier.pubmed |
32051003 |
|
dc.contributor.department |
SE/AOK/I/Élettani Intézet |
|
dc.contributor.institution |
Semmelweis Egyetem |
|