DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions.

ABSTRACT: Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential

SUBMITTER: Klambauer G 

PROVIDER: S-EPMC3834838 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature