Significance analysis of groups of genes in expression profiling studies
Significance analysis of groups of genes in expression profiling studies
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DOI：
10.1093/bioinformatics/btm310
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发表时间：
2007-08-15
期刊：
影响因子：
5.8
通讯作者：
Tsai, Chen-An
中科院分区：
文献类型：
--
作者：
Chen, James J.;Lee, Taewon;Tsai, Chen-An
动机：基因类别测试（GCT）是一种统计方法，用于确定某些功能上预定义的基因类别在两种实验条件下是否有不同的表达。 GCT 根据零分布计算每个基因类别的 P 值，并根据其 P 值对基因类别的重要性进行排名。目前，已经考虑了两个零假设：Q1 假设测试基因类别之间与表型关联的相对强度，Q2 假设评估统计显着性。这两个假设相关但不等价。 方法：我们在 Q1 和 Q2 下调查三个单边和两个双边检验统计量。 Q1 下基因类别的零分布是通过排列基因标签生成的，Q2 下基因类别的零分布是通过排列样本生成的。结果：我们将五个统计数据应用于包含 143 个基因类别的糖尿病数据集和包含 508 个 GO（基因本体）术语的乳腺癌数据集。在每个统计中，两个数据集中 Q1 下的基因类的零分布与 Q2 下的基因类的零分布不同，并且它们的排名也可能不同。我们澄清了单边和双边假设，并讨论了有关 GCT 中基因类别排名的 Q1 和 Q2 假设的一些问题。因为Q1不处理基因之间的相关性，所以我们更喜欢基于Q2进行测试。联系方式：jchen@nctr.fda.gov补充信息：补充数据可在生物信息学在线获得。
Motivation: Gene class testing (GCT) is a statistical approach to determine whether some functionally predefined classes of genes express differently under two experimental conditions. GCT computes the P-value of each gene class based on the null distribution and the gene classes are ranked for importance in accordance with their P-values. Currently, two null hypotheses have been considered: the Q1 hypothesis tests the relative strength of association with the phenotypes among the gene classes, and the Q2 hypothesis assesses the statistical significance. These two hypotheses are related but not equivalent.Method: We investigate three one-sided and two two-sided test statistics under Q1 and Q2. The null distributions of gene classes under Q1 are generated by permuting gene labels and the null distributions under Q2 are generated by permuting samples.Results: We applied the five statistics to a diabetes dataset with 143 gene classes and to a breast cancer dataset with 508 GO ( Gene Ontology) terms. In each statistic, the null distributions of the gene classes under Q1 are different from those under Q2 in both datasets, and their rankings can be different too. We clarify the one-sided and two-sided hypotheses, and discuss some issues regarding the Q1 and Q2 hypotheses for gene class ranking in the GCT. Because Q1 does not deal with correlations among genes, we prefer test based on Q2.Contact: jchen@nctr.fda.govSupplementary information: Supplementary data are available at Bioinformatics online.