A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.

ABSTRACT: Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-va

SUBMITTER: Vasiliu D 

PROVIDER: S-EPMC4349782 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature