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

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  • 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 2020-03-27T09:26:53Z
  • dc.date.available 2020-03-27T09:26:53Z
  • dc.date.issued 2020
  • dc.description.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.
  • dc.description.sponsorship CHH is supported by the German Federal Ministry of Education and Research (BMBF)-funded project Systems Medicine of the Liver (LiSyM, FKZ: 031 L0049). MPK, BAJ, and DAL are supported by NIH Grant U54-CA217377. BS is supported by the Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. HH is a Miguel Servet (CP14/00229) researcher funded by the Spanish Institute of Health Carlos III (ISCIII). This work has received funding from the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE)
  • dc.format.mimetype application/pdf
  • dc.identifier.citation 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.
  • dc.identifier.doi http://dx.doi.org/10.1186/s13059-020-1949-z
  • dc.identifier.issn 1474-7596
  • dc.identifier.uri http://hdl.handle.net/10230/44064
  • dc.language.iso eng
  • dc.publisher BioMed Central
  • dc.relation.ispartof Genome Biology. 2020 Feb 12;21(1):36
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/SAF2017-89109-P
  • dc.rights © Christian H. Holland et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
  • dc.subject.other Genètica
  • dc.subject.other Seqüència de nucleòtids
  • dc.subject.other Benchmark
  • dc.title Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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