Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets.
Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets.
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DOI：
10.1186/1471-2105-11-19
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发表时间：
2010-01-11
影响因子：
3
通讯作者：
Kipling D
中科院分区：
文献类型：
--
作者：
Czwan E;Brors B;Kipling D
主题驱动的癌症生存研究解决了与生物过程相关的基因的表达特征是否可以预测患者的生存时间。虽然这应该通过测试两个独立的零假设来实现，但目前的方法将两个假设视为一个。第一个测试应该评估是否一个基因，独立于其组成，与预后（通常与生存测试）。然后第二个测试验证类集的主题是否相关（通常通过将感兴趣的类集与随机类集进行比较的经验测试来完成）。目前的方法不测试该第二个零假设，因为已经假设随机基因组的p值的分布（当针对第一个零假设进行测试时）是均匀的。在这里，我们证明了这种假设通常是不正确的，因此，这种方法可能错误地将特定基因的生物学与癌症预后联系起来。为了评估随机基因组的非均匀分布在这些研究中的影响，开发了一种自动主题驱动的方法。该方法基于置换方法经验地近似不相关基因组的p值分布，并测试预定义的生物相关基因组是否与生存相关。与已发表的主题驱动方法进行比较的结果显示了非均匀分布，这表明原始研究中存在假阳性率的重大问题。当应用于两个公开的癌症数据集时，我们的技术揭示了具有预后能力的新型本体分类，包括“脂肪酸代谢”与乳腺癌总生存率之间的显著相关性，以及“受体介导的内吞作用”、“脑发育”、“顶端质膜”和“MAPK信号通路”与肺癌总生存率之间的显著相关性。目前的主题驱动生存研究方法假设随机基因组的p值是一致的，这可能导致错误的结论。我们的方法提供了一种纠正这种缺陷的方法，并提供了一种新的途径，以确定更高层次的生物主题和途径与临床微阵列数据集的预后能力。
Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. Current methods of theme-driven survival studies assume uniformity of p-values for random genesets, which can lead to false conclusions. Our approach provides a method to correct for this pitfall, and provides a novel route to identifying higher-level biological themes and pathways with prognostic power in clinical microarray datasets.
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影响因子：
3
作者：
Lu, Xin;Perkins, David L.
通讯作者：
Perkins, David L.
影响因子：
4.7
作者：
Haluska, Frank G;Ibrahim, Nageatte
通讯作者：
Ibrahim, Nageatte
斯坦福微阵列数据库：新分析工具的实现和软件的开源发布。
影响因子：
14.9
作者：
Demeter, Janos;Beauheim, Catherine;Gollub, Jeremy;Hernandez-Boussard, Tina;Jin, Heng;Maier, Donald;Matese, John C.;Nitzberg, Michael;Wymore, Farrell;Zachariah, Zachariah K.;Brown, Patrick O.;Sherlock, Gavin;Ball, Catherine A.
通讯作者：
Ball, Catherine A.
影响因子：
3
作者：
Alibés A;Yankilevich P;Cañada A;Díaz-Uriarte R
通讯作者：
Díaz-Uriarte R
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y