Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

ABSTRACT: RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the add

SUBMITTER: Jia Z 

PROVIDER: S-EPMC4562600 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature