Abstract
BackgroundIn 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.ResultsWe 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.ConclusionThe MTGDR provides an effective way of analyzing multiple cancer microarray studies and selecting reliable cancer-associated genes.
Highlights
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
Simulation study We conduct simulation studies to investigate the performance of the proposed Meta Threshold Gradient Descent Regularization (MTGDR)
The regression coefficients for the cancer-associated genes vary across studies, which corresponds to different experimental setups in different studies
Summary
It is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. 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. It is generally accepted that only meta analysis can circumvent the problems inherent to studies with low statistical powers due to low sample sizes [1]. With meta analysis, it is usually not the intention of researchers to analyze any new datasets. It provides an effective way of pooling and analyz-
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