GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

ABSTRACT:

Background

Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes.

Methodology

A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simul

SUBMITTER: Mpindi JP 

PROVIDER: S-EPMC3041823 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature