Methodology article

Between-groups within-gene heterogeneity of residual variances in microarray gene expression data

Joaquim Casellas¹ and Luis Varona^1,2

¹  Genètica i Millora Animal, IRTA-Lleida, 25198 Lleida, Spain

²  Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain

author email corresponding author email

BMC Genomics 2008, 9:319doi:10.1186/1471-2164-9-319

Published:	4 July 2008

Abstract

Background

The analysis of microarray gene expression data typically tries to identify differential gene expression patterns in terms of differences of the mathematical expectation between groups of arrays (e.g. treatments or biological conditions). Nevertheless, the differential expression pattern could also be characterized by group-specific dispersion patterns, although little is known about this phenomenon in microarray data. Commonly, a homogeneous gene-specific residual variance is assumed in hierarchical mixed models for gene expression data, although it could result in substantial biases if this assumption is not true.

Results

In this manuscript, a hierarchical mixed model with within-gene heterogeneous residual variances is proposed to analyze gene expression data from non-competitive hybridized microarrays. Moreover, a straightforward Bayes factor is adapted to easily check within-gene (between groups) heterogeneity of residual variances when samples are grouped in two different treatments. This Bayes factor only requires the analysis of the complex model (hierarchical mixed model with between-groups heterogeneous residual variances for all analyzed genes) and gene-specific Bayes factors are provided from the output of a simple Markov chain Monte Carlo sampling.

Conclusion

This statistical development opens new research possibilities within the gene expression framework, where heterogeneity in residual variability could be viewed as an alternative and plausible characterization of differential expression patterns.