Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes

BMC Bioinformatics
Hongying JiangShuanglin Zhang

Abstract

Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were...Continue Reading

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Citations

Sep 20, 2007·Functional & Integrative Genomics·Erin M Conlon
May 28, 2005·Nature Genetics·Daniel R Rhodes, Arul M Chinnaiyan
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Methods Mentioned

BETA
PCA
chips
chip

Software Mentioned

exe
GSFLD
dChip
HCA
disTran
survfit
Affymetrix
coxph
GSRF
R

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