Two-phase differential expression analysis for single cell RNA-seq.

Document Type

Article

Publication Date

10-1-2018

JAX Source

Bioinformatics 2018 Oct 1; 34(19):3340-3348

Volume

34

Issue

19

First Page

3340

Last Page

3348

ISSN

1367-4811

PMID

29688282

DOI

https://doi.org/10.1093/bioinformatics/bty329

Grant

Single cell transcriptome work in the Stitzel Lab is supported by the Assistant Secretary of Defense for Health Affairs, through the Peer Reviewed Medical Research Program under Award No. W81XWH-16-1-0130.

Abstract

Motivation: Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of 'differential expression'. However, most computational methods still stick with the old mentality of viewing differential expression as a simple 'up or down' phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations.

Results: We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes.

Availability and implementation: SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P.

Supplementary information: Supplementary data are available at Bioinformatics online.

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