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

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

研究背景：我们对既往发表的一项数据集开展了统计分析，该数据集取自两种小鼠品系的六个不同脑区，为基因表达微阵列数据（gene expression microarray data）。既往分析中，研究人员已鉴定出24个在品系间存在表达差异的基因，以及约240个存在脑区表达差异的基因。正如多数基因表达研究一样，该既往分析主要依赖特设的‘倍数变化（fold change）’与‘存在/缺失（absent/present）’标准来筛选基因。为明确基于统计原理的分析方法能否对脑内基因表达模式实现更灵敏且更具特异性的分析，本研究采用方差分析（Analysis of Variance, ANOVA）与特征选择方法（feature selection methods），以筛选出呈现品系依赖性或脑区依赖性表达模式的基因。

研究结果：本分析发现了更多潜在与两种小鼠品系间行为差异及六个脑区间功能差异相关的基因。采用保守统计标准，我们鉴定出至少63个存在品系表达差异的基因，以及约600个存在脑区表达差异的基因。与特设筛选方法不同，本研究的方法可通过统计得分对基因进行排序，使后续分析能够聚焦于最具统计学意义的基因。将本研究结果与既往研究及已发表的单基因研究报告进行比对后发现，本研究在保证分析特异性的同时实现了更高的检测灵敏度。

研究结论：本研究结果表明，所研究的小鼠品系间及脑区间的分子差异规模大于既往研究所揭示的水平。综上，对于大型复杂数据集，方差分析（ANOVA）与特征选择方法（单独使用或联合使用）的分析效能均优于基于倍数变化阈值及其他特设筛选标准的方法。