Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
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 chara...
| Main Authors: | , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central
2020
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| Online Access: | https://hdl.handle.net/1721.1/126321 |
| _version_ | 1826215559013859328 |
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| author | Holland, Christian H Tanevski, Jovan Perales-Patón, Javier Gleixner, Jan Kumar, Manu Prajapati Mereu, Elisabetta Joughin, Brian Alan Stegle, Oliver Lauffenburger, Douglas A Heyn, Holger Szalai, Bence Saez-Rodriguez, Julio |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Holland, Christian H Tanevski, Jovan Perales-Patón, Javier Gleixner, Jan Kumar, Manu Prajapati Mereu, Elisabetta Joughin, Brian Alan Stegle, Oliver Lauffenburger, Douglas A Heyn, Holger Szalai, Bence Saez-Rodriguez, Julio |
| author_sort | Holland, Christian H |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:35:08Z |
| format | Article |
| id | mit-1721.1/126321 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:35:08Z |
| publishDate | 2020 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1263212022-10-02T08:20:37Z Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data Holland, Christian H Tanevski, Jovan Perales-Patón, Javier Gleixner, Jan Kumar, Manu Prajapati Mereu, Elisabetta Joughin, Brian Alan Stegle, Oliver Lauffenburger, Douglas A Heyn, Holger Szalai, Bence Saez-Rodriguez, Julio Massachusetts Institute of Technology. Department of Biological Engineering Koch Institute for Integrative Cancer Research at MIT 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. NIH (Grant U54-CA217377) Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE) 2020-07-22T19:03:31Z 2020-07-22T19:03:31Z 2020-02-12 2019-09 2020-06-26T11:08:39Z Article http://purl.org/eprint/type/JournalArticle 1474-760X https://hdl.handle.net/1721.1/126321 Holland, Christian H. et al. "Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data." Genome Biology 36 (Feb. 2020): 36 doi 10.1186/s13059-020-1949-z ©2020 Author(s) en 10.1186/s13059-020-1949-z Genome Biology Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s). application/pdf BioMed Central BioMed Central |
| spellingShingle | Holland, Christian H Tanevski, Jovan Perales-Patón, Javier Gleixner, Jan Kumar, Manu Prajapati Mereu, Elisabetta Joughin, Brian Alan Stegle, Oliver Lauffenburger, Douglas A Heyn, Holger Szalai, Bence Saez-Rodriguez, Julio Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title | Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title_full | Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title_fullStr | Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title_full_unstemmed | Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title_short | Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data |
| title_sort | robustness and applicability of transcription factor and pathway analysis tools on single cell rna seq data |
| url | https://hdl.handle.net/1721.1/126321 |
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