Fold change and p-value cutoffs significantly alter microarray interpretations.

ABSTRACT:

Background

As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but y

SUBMITTER: Dalman MR 

PROVIDER: S-EPMC3305783 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature