Expression-based Pathway Signature Analysis (EPSA): mining publicly available microarray data for insight into human disease.

ABSTRACT:

Background

Publicly available data repositories facilitate the sharing of an ever-increasing amount of microarray data. However, these datasets remain highly underutilized. Reutilizing the data could offer insights into questions and diseases entirely distinct from those considered in the original experimental design.

Methods

We first analyzed microarray datasets derived from known perturbations of specific pathways using the samr package in R to identify specific patterns of change in gene expression. We refer to these pattern of gene expression alteration as a "pathway signatures." We then used Spearman's rank correlation coefficient, a non-parametric measure of correlation, to determine similarities between pathway signatures and disease profiles, and permutation analysis

SUBMITTER: Tenenbaum JD 

PROVIDER: S-EPMC2588448 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature