Analysis of strain and regional variation in gene expression in mouse brain.
Analysis of strain and regional variation in gene expression in mouse brain.
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DOI：
10.1186/gb-2001-2-10-research0042
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发表时间：
2001
期刊：
影响因子：
12.3
通讯作者：
Noble WS
中科院分区：
文献类型：
--
作者：
Pavlidis P;Noble WS
我们对先前发表的一组来自两个小鼠品系的六个不同大脑区域的基因表达微阵列数据进行了统计分析。在之前的分析中，鉴定出菌株间表达差异基因24个，区域表达差异基因约240个。与许多基因表达研究一样，该分析主要依赖于特别的“折叠变化”和“缺失/存在”标准来选择基因。为了确定统计动机的方法是否能够对大脑中的基因表达模式进行更敏感和选择性的分析，我们决定使用方差分析（ANOVA）和特征选择方法，旨在选择显示菌株或区域依赖表达模式的基因。我们的分析揭示了许多额外的基因，这些基因可能与两种小鼠品系之间的行为差异和六个大脑区域之间的功能差异有关。使用保守的统计标准，我们确定了至少63个基因表现出菌株变异，大约600个基因表现出区域变异。与特别方法不同，我们的方法还有一个额外的好处，即通过统计得分对基因进行排序，从而允许进一步分析，重点关注最重要的基因。将我们的结果与先前的研究和已发表的关于单个基因的报告进行比较，表明我们在保持选择性的同时实现了高灵敏度。我们的结果表明，菌株和研究区域之间的分子差异比以前指出的要大。我们的结论是，对于大型复杂数据集，方差分析和特征选择，单独或组合，比基于倍数变化阈值和其他特别选择标准的方法更强大。
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.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
4.8
作者：
Long, AD;Mangalam, HJ;Baldi, P
通讯作者：
Baldi, P
影响因子：
4.7
作者：
Nakayama, T;Yaoi, T;Kuwajima, G
通讯作者：
Kuwajima, G
影响因子：
7
作者：
Callow, MJ;Dudoit, S;Rubin, EM
通讯作者：
Rubin, EM