Abstract
Differential expression analysis is an important aspect of bulk RNA sequencing (RNAseq). A lot of tools are available, and among them DESeq2 and edgeR are widely used. Since single-cell RNA sequencing (scRNAseq) expression data are zero inflated, single-cell data are quite different from those generated by conventional bulk RNA sequencing. Comparative analysis of tools used to detect differentially expressed genes between two groups of single cells showed that edgeR with quasi-likelihood F-test (QLF) outperforms other methods.
In bulk RNAseq, differential expression is mainly used to compare limited number of replicates of two or more biological conditions. However, scRNAseq differential expression analysis might be also instrumental to identify the main players of cells subpopulation organization, thus requiring the use of multiple comparisons tools. Nowadays, edgeR is one of the few tools that are able to handle both zero inflated matrices and multiple comparisons. Here, we provide a guide to the use of edgeR as a tool to detect differential expression in single-cell data.
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Alessandrì, L., Arigoni, M., Calogero, R. (2019). Differential Expression Analysis in Single-Cell Transcriptomics. In: Proserpio, V. (eds) Single Cell Methods. Methods in Molecular Biology, vol 1979. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9240-9_25
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DOI: https://doi.org/10.1007/978-1-4939-9240-9_25
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