期刊
GENOME BIOLOGY
卷 16, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-015-0844-5
关键词
Bimodality; Cellular detection rate; Co-expression; Empirical Bayes; Generalized linear model; Gene set enrichment analysis
资金
- NIH [DP2 DE023321, R01 EB008400]
- Bill and Melinda Gates Foundation [OPP1032317]
- Bill and Melinda Gates Foundation [OPP1032317] Funding Source: Bill and Melinda Gates Foundation
Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment.
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