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Analysis of strain and regional variation in gene expression in mouse brain

Paul Pavlidis1* and William S Noble23

Author Affiliations

1 Columbia Genome Center, Columbia University, 1150 St Nicholas Avenue, New York, NY 10032, USA

2 Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA

3 Previous name: William Noble Grundy

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Genome Biology 2001, 2:research0042-research0042.15  doi:10.1186/gb-2001-2-10-research0042

Published: 27 September 2001



We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression.


Our analysis revealed many additional genes that might be involved in behavioral differences between the two mouse strains and functional differences between the six brain regions. Using conservative statistical criteria, we identified at least 63 genes showing strain variation and approximately 600 genes showing regional variation. Unlike ad hoc methods, ours have the additional benefit of ranking the genes by statistical score, permitting further analysis to focus on the most significant. Comparison of our results to the previous studies and to published reports on individual genes show that we achieved high sensitivity while preserving selectivity.


Our results indicate that molecular differences between the strains and regions studied are larger than indicated previously. We conclude that for large complex datasets, ANOVA and feature selection, alone or in combination, are more powerful than methods based on fold-change thresholds and other ad hoc selection criteria.