Making sense of human lung carcinomas gene expression data: Integration and analysis of two affymetrix platform experiments

High throughput technologies such as microarray, mass spectrometry and nuclear magnetic resonance, have generated large volumes of valuable data for biology research. Researchers often face the challenges of integrating data from different sources and of identifying potential biomarkers that are highly associated with disease, drug safety, and efficacy. We present several solutions to these challenges through two Affymetrix miroarray studies aimed at providing new insights into lung cancer biology. The Harvard dataset and the Michigan dataset were integrated to identify genes that were predictive of cancer survival. Quantile normalization of expression measures was applied to make the two datasets comparable. Genes highly associated with survival were identified and survival tree analysis on the combined data was performed to predict mortality. The candidate genes could be useful for lung cancer disease prediction and cancer therapy. The methodologies for integration and analysis of multiple gene expression data have been shown to perform well and could be generalized to broader applications.