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DGEclust: differential expression analysis of clustered count data
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 supporte...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365804/ https://www.ncbi.nlm.nih.gov/pubmed/25853652 http://dx.doi.org/10.1186/s13059-015-0604-6 |
Sumario: | 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0604-6) contains supplementary material, which is available to authorized users. |
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