Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer [chapter]

Jeffrey S. Morris, Guosheng Yin, Keith Baggerly, Chunlei Wu, Li Zhang
Methods of Microarray Data Analysis  
Our goal in this work is to pool information across microarray studies conducted at different institutions using two different versions of Affymetrix chips to identify genes whose expression levels offer information on lung cancer patients' survival above and beyond the information provided by readily available clinical covariates. We combine information across chip types by identifying "matching probes" present on both chips, and then assembling them into new probesets based on Unigene
more » ... . This method yields comparable expression level quantifications across chips without sacrificing much precision or significantly altering the relative ordering of the samples. We fit a series of multivariable Cox models containing clinical covariates and genes and identify 26 genes that provide information on survival after adjusting for the clinical covariates, while controlling the false discovery rate at 0.20 using the Beta-Uniform mixture method. Many of these genes appear to be biologically interesting and worthy of future investigation. Only one gene in our list has been mentioned in previously published analyses of these data. It appears that the increased statistical power provided by the pooling is key to finding these new genes, since only 9 out of the 26 genes are detected when we apply these methods to the two data sets separately, i.e., without pooling.
doi:10.1007/0-387-23077-7_5 fatcat:jn6rhexvknfynbnxoglt5sz7la