Regularized gene selection in cancer microarray meta-analysis
Regularized gene selection in cancer microarray meta-analysis
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DOI：
10.1186/1471-2105-10-1
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发表时间：
2009-01-01
影响因子：
3
通讯作者：
Huang, Jian
中科院分区：
文献类型：
--
作者：
Ma, Shuangge;Huang, Jian
背景资料：在癌症研究中，通常进行多个微阵列实验来测量相同基因组的相同临床结果和表达。这些实验的一个重要目标是确定一个基因子集，这些基因可以潜在地作为癌症发展和进展的预测标志物。由于样本量小，对单个实验的分析可能导致不可靠的基因选择结果。Meta分析可用于汇集多个实验，增加统计功效，并实现更可靠的基因选择。癌症微阵列数据的荟萃分析是具有挑战性的，因为基因表达的高维性和不同experiments.Results之间的实验设置的差异：我们提出了一个Meta阈值梯度下降正则化（MTGDR）方法在癌症微阵列数据的荟萃分析基因选择。与现有办法相比，《中期报告》有许多优点。它允许不同的实验具有不同的实验设置。它可以解释多个基因对癌症的联合作用，并且可以在多个实验中选择相同的癌症相关基因。多个胰腺癌和肝癌实验的模拟研究和分析表明，上级性能的MTGDR。结论：MTGDR提供了一种有效的方法来分析多个癌症芯片研究和选择可靠的癌症相关基因。
Background: In cancer studies, it is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. An important goal of such experiments is to identify a subset of genes that can potentially serve as predictive markers for cancer development and progression. Analyses of individual experiments may lead to unreliable gene selection results because of the small sample sizes. Meta analysis can be used to pool multiple experiments, increase statistical power, and achieve more reliable gene selection. The meta analysis of cancer microarray data is challenging because of the high dimensionality of gene expressions and the differences in experimental settings amongst different experiments.Results: We propose a Meta Threshold Gradient Descent Regularization (MTGDR) approach for gene selection in the meta analysis of cancer microarray data. The MTGDR has many advantages over existing approaches. It allows different experiments to have different experimental settings. It can account for the joint effects of multiple genes on cancer, and it can select the same set of cancer-associated genes across multiple experiments. Simulation studies and analyses of multiple pancreatic and liver cancer experiments demonstrate the superior performance of the MTGDR.Conclusion: The MTGDR provides an effective way of analyzing multiple cancer microarray studies and selecting reliable cancer-associated genes.