Identifying statistically significant combinatorial markers for survival analysis.
Identifying statistically significant combinatorial markers for survival analysis.
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DOI：
10.1186/s12920-018-0346-x
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发表时间：
2018-04-20
影响因子：
2.7
通讯作者：
Sese J
中科院分区：
文献类型：
--
作者：
Relator RT;Terada A;Sese J
生存分析方法已广泛应用于健康和医学的不同领域，涵盖各种感兴趣的事件和目标疾病。它们可用于提供个体的生存时间与感兴趣的因素之间的关系，使其可用于寻找癌症等疾病的生物标志物。然而，一些疾病的进展可能是非常不可预测的，因为传统的方法未能考虑多标记物的相互作用。候选标志物数量的指数增加在多重检验校正中需要大的校正因子并且隐藏显著性。我们解决的问题，通过调整最近开发的无限Arity多重检验程序（LAMP），P值校正技术的统计测试组合的标志物的组合，影响生存的测试标志物组合。LAMP无法处理生存数据统计，因此我们扩展了LAMP用于对数秩检验，使其更适合临床数据，并新引入了p值的理论下限。我们将所提出的方法应用于癌症的基因组合检测，并获得了具有统计学显著性对数秩p值的基因相互作用。我们的算法检测到的基因组合的订单多达32个基因，在这些组合中的一些基因的影响也支持现有的文献。本文提出的检测预后标志物的新方法可以识别统计学显著的标志物，对相互作用的顺序没有限制。此外，它可以应用于不同类型的基因组数据，只要二进制化是可能的。
Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance. We address the issue of testing marker combinations that affect survival by adapting the recently developed Limitless Arity Multiple-testing Procedure (LAMP), a p-value correction technique for statistical tests for combination of markers. LAMP cannot handle survival data statistics, and hence we extended LAMP for the log-rank test, making it more appropriate for clinical data, with newly introduced theoretical lower bound of the p-value. We applied the proposed method to gene combination detection for cancer and obtained gene interactions with statistically significant log-rank p-values. Gene combinations with orders of up to 32 genes were detected by our algorithm, and effects of some genes in these combinations are also supported by existing literature. The novel approach for detecting prognostic markers presented here can identify statistically significant markers with no limitations on the order of interaction. Furthermore, it can be applied to different types of genomic data, provided that binarization is possible.
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DOI：
10.1093/bioinformatics/btt532
发表时间：
2013-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Duverle DA;Takeuchi I;Murakami-Tonami Y;Kadomatsu K;Tsuda K
通讯作者：
Tsuda K
影响因子：
4
作者：
Salhab, Mohamed;Patani, Neill;Mokbel, Kefah
通讯作者：
Mokbel, Kefah
多阶段全基因组关联研究确定了七个前列腺癌易感位点
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
3.8
作者：
Itoh, Mitsuya;Iwamoto, Takayuki;Pusztai, Lajos
通讯作者：
Pusztai, Lajos
影响因子：
11.1
作者：
Sircoulomb, Fabrice;Nicolas, Nathalie;Ferrari, Anthony;Finetti, Pascal;Bekhouche, Ismahane;Rousselet, Estelle;Lonigro, Aurelie;Adelaide, Jose;Baudelet, Emilie;Esteyries, Severine;Wicinski, Julien;Audebert, Stephane;Charafe-Jauffret, Emmanuelle;Jacquemier, Jocelyne;Lopez, Marc;Borg, Jean-Paul;Sotirious, Christos;Popovici, Cornel;Bertucci, Francois;Birnbaum, Daniel;Chaffanet, Max;Ginestier, Christophe
通讯作者：
Ginestier, Christophe