Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.

ABSTRACT:

Background

With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.

Results

We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the p

SUBMITTER: Hu P 

PROVIDER: S-EPMC1173085 | biostudies-literature | 2005 May

REPOSITORIES: biostudies-literature