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We present a statistical methodology, DGEclust, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supported by the data and uncertainty in parameter estimation. DGEclust successfully identifies differentially expressed genes under a number of different scenarios, maintaining a low error rate and an excellent control of its false discovery rate with reasonable computational requirements. It is formulated to perform particularly well on low-replicated data and be applicable to multi-group data. DGEclust is available at http://dvav.github.io/dgeclust/.

Original publication

DOI

10.1186/s13059-015-0604-6

Type

Journal article

Journal

Genome Biol

Publication Date

20/02/2015

Volume

16

Keywords

Algorithms, Cluster Analysis, Gene Expression, Gene Expression Profiling, Humans, Oligonucleotide Array Sequence Analysis, Software