Regularized gene selection in cancer microarray meta-analysis

Shuangge Ma; Jian Huang

doi:10.1186/1471-2105-10-1

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Regularized gene selection in cancer microarray meta-analysis

Journal article

Open access

Peer reviewed

Regularized gene selection in cancer microarray meta-analysis

Shuangge Ma and Jian Huang

BMC bioinformatics, Vol.10(1), pp.1-1

01/01/2009

DOI: 10.1186/1471-2105-10-1

PMCID: PMC2631520

PMID: 19118496

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Abstract

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. 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. The MTGDR provides an effective way of analyzing multiple cancer microarray studies and selecting reliable cancer-associated genes.

Neoplasms - metabolism

Computational Biology - methods

Pancreatic Neoplasms - metabolism

Liver Neoplasms - genetics

Oligonucleotide Array Sequence Analysis - methods

Humans

Gene Expression Regulation, Neoplastic

Gene Expression Profiling - methods

Male

Pancreatic Neoplasms - genetics

Biomarkers, Tumor

Algorithms

Neoplasms - genetics

Computer Simulation

Bayes Theorem

Liver Neoplasms - metabolism

Female

Cluster Analysis

Details

Title: Subtitle: Regularized gene selection in cancer microarray meta-analysis
Creators: Shuangge Ma - Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu
Jian Huang
Resource Type: Journal article
Publication Details: BMC bioinformatics, Vol.10(1), pp.1-1
DOI: 10.1186/1471-2105-10-1
PMID: 19118496
PMCID: PMC2631520
NLM abbreviation: BMC Bioinformatics
ISSN: 1471-2105
eISSN: 1471-2105
Publisher: England
Grant note: R03LM009828 / NLM NIH HHS R01 CA120988 / NCI NIH HHS R03 LM009828 / NLM NIH HHS R01CA120988 / NCI NIH HHS
Language: English
Date published: 01/01/2009
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9983985929802771

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