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

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 proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also ...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Pingzhao Hu, Celia M. T. Greenwood, Joseph Beyene
• Keywords: BMCBI 2005, Effect Size, Expressed Genes, Gene Expression