PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer
PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer
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DOI：
10.1093/bioinformatics/btl174
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发表时间：
2006-09-15
期刊：
影响因子：
5.8
通讯作者：
Caldas, Carlos
中科院分区：
文献类型：
--
作者：
Teschendorff, Andrew E.;Naderi, Ali;Caldas, Carlos
动机：由于需要测量大量变量，阐明癌症的分子分类并从微阵列实验中寻找生物和临床标记物是有问题的。因此，需要能够识别相关分类器或在监督分析之前消除可能的误报的特征选择方法。结果：我们提出了一种基于混合模型和基因表达谱的非高斯性测量的新颖特征选择程序。该方法可用于查找定义小的异常值子组或主要细分的基因，具体取决于峰度的符号。该方法还可以用作监督分析之前的过滤步骤，以降低错误发现率。我们通过重新发现 ER 阴性和 ER 阳性乳腺癌以及前列腺癌的主要分类器，使用六个独立数据集验证了我们的方法。此外，我们的方法在 ER 阴性乳腺肿瘤的基底亚组中发现了两种新的亚型，分别与细胞凋亡和免疫反应功能相关，并且具有统计学上不同的临床结果。
Motivation: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable.Results: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome.