Seq-ing improved gene expression estimates from microarrays using machine learning.

ABSTRACT:

Background

Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories.

Results

We propose a novel approach to microarray analysis that attains many of the advantages of RNA-Seq. This method, called Machine Learning of Transcript Expression (MaLTE), leverages samples for which both microarray and RNA-Seq data are available, using a Random Forest to learn the relationship between the fluorescence intensity of sets of microarray probes and RNA-Seq transcript expression estimates. We trained MaLTE on data from the Gen

SUBMITTER: Korir PK 

PROVIDER: S-EPMC4559919 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature