Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

ABSTRACT: Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpres

SUBMITTER: Wu S 

PROVIDER: S-EPMC4843452 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature