Differential Expression

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Reveal statistically significant genes and transcripts from count matrices.

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Using RNA-seq to generate matrices of transcript and gene abundances has become a staple technique for measuring cell state.¹ Often it is desirable to use statistical techniques to compare these count matrices across different experimental conditions to reveal genes that change.²

A software benchmark conducted by Costa Silva et. al revealed DESeq2 to be the most performant. For each of a list of tools, reported significant genes were compared against ground-truth genes derived from qRT-PCR, and DESeq2 consistently showed the highest sensitivity (TPR) and accuracy.

Footnotes

1. Stark, Rory; Grzelak, Marta; Hadfield, James (2019). RNA sequencing: the teenage years. Nature Reviews Genetics, (), –. doi:10.1038/s41576-019-0150-2 ↩

2. Costa-Silva J, Domingues D, Lopes FM (2017) RNA-Seq differential expression analysis: An extended review and a software tool. PLoS ONE 12(12): e0190152. https://doi.org/10.1371/journal.pone.0190152 ↩