Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
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 s...
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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
12 February 2020
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| In: |
Genome biology
Year: 2020, Jahrgang: 21 |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-020-1949-z |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s13059-020-1949-z |
| Verfasserangaben: | Christian H. Holland, Jovan Tanevski, Javier Perales-Patón, Jan Gleixner, Manu P. Kumar, Elisabetta Mereu, Brian A. Joughin, Oliver Stegle, Douglas A. Lauffenburger, Holger Heyn, Bence Szalai and Julio Saez-Rodriguez |
| Zusammenfassung: | 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. |
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| Beschreibung: | Gesehen am 02.04.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-020-1949-z |