Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes.

ABSTRACT:

Introduction

Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.

Methods

We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.

Results</

SUBMITTER: Marko NF 

PROVIDER: S-EPMC3485292 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature