Bayesian approach to single-cell differential expression analysis
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Updated: Apr 29, 2026
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
Peter V Kharchenko1, Lev Silberstein2, David T Scadden2
11] Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. [2] Hematology/Oncology Program, Children's Hospital, Boston, Massachusetts, USA. [3] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA.
Abstract:
Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.
