Bayesian approach to single-cell differential expression analysis

• Published in: Nature methods Vol. 11; no. 7; pp. 740 - 742
• Main Authors: Kharchenko, Peter V, Silberstein, Lev, Scadden, David T
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.07.2014; Nature Publishing Group
• Subjects: 38/91; 631/114/2415; 631/1647/2217/2218; 631/208/212/2019; Algorithms; Analysis; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; brief-communication; Cell differentiation; Cellular biology; Gene expression; Gene Expression Profiling - methods; Life Sciences; Oligonucleotide Array Sequence Analysis; Physiological aspects; Protein expression; Proteomics; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Subpopulations
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.2967

A method to model expression variability in single-cell RNA-seq measurements and thus to improve subsequent data analysis. 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.