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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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
12 February 2020
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| In: |
Genome biology
Year: 2020, Volume: 21 |
| ISSN: | 1474-760X |
| DOI: | 10.1186/s13059-020-1949-z |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s13059-020-1949-z |
| Author Notes: | 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 |
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| 520 | |a 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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