Method designed to respect molecular heterogeneity can profoundly correct present data interpretations for genome-wide expression analysis.

ABSTRACT: Although genome-wide expression analysis has become a routine tool for gaining insight into molecular mechanisms, extraction of information remains a major challenge. It has been unclear why standard statistical methods, such as the t-test and ANOVA, often lead to low levels of reproducibility, how likely applying fold-change cutoffs to enhance reproducibility is to miss key signals, and how adversely using such methods has affected data interpretations. We broadly examined expression data to investigate the reproducibility problem and discovered that molecular heterogeneity, a biological property of genetically different samples, has been improperly handled by the statistical methods. Here we give a mathematical description of the discovery and report the development of a statistical meth

SUBMITTER: Chen CH 

PROVIDER: S-EPMC4368820 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature