Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan.
Comput Math Methods Med. 2013;2013:693901. doi: 10.1155/2013/693901. Epub 2013 Apr 10.
Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.
癌症的分子异质性，部分由各种染色体异常或基因突变引起，可导致癌症样本中基因表达谱的显著异质性。为了检测仅在一部分癌症样本或癌症离群值中活跃的癌症相关基因，在多重检验的背景下已经提出了几种方法。与标准的多重检验设置相比，这种癌症离群值分析通常会严重缺乏功效，在标准的多重检验设置中，假定所有癌症样本中基因的常见激活。在本文中，我们考虑了基因和癌症样本之间的信息共享，通过对标准化后的参考正常样本数据进行基因表达水平的参数正态混合建模。基于每个癌症样本的癌症离群后验概率，开发了一种基于基因的基因选择统计量。即使在指定的、长尾 t 分布下，使用我们的方法也能提高效率。提供了一个来自血液恶性肿瘤的真实数据集的应用。
