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bioRxiv · 10.1101/173997

DEsingle: A new method for single-cell differentially expressed genes detection and classification

Miao, Z.·Zhang, X.
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Abstract

SummaryThe excessive amount of zeros in single-cell RNA-seq data include \"real\" zeros due to the on-off nature of gene transcription in single cells and \"dropout\" zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.\n\nAvailability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductors consideration now.\n\nContactzhangxg@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at bioRxiv online.

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Miao, Z., Zhang, X.. 2017-08-09. DEsingle: A new method for single-cell differentially expressed genes detection and classification. https://doi.org/10.1101/173997

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