A New Approach to Identify Differentially Expressed Genes by Integrating Cancer Microarray and SAGE Data

摘要

Microarray and SAGE are the most common approaches of revealing gene expression profile and of identifying differentially expressed genes (DEGs). Due to the differences in the techniques of the two data sources, and in the data processing methods, the data generated by microarray and SAGE are usually treated separately. Nevertheless, the integration of the two data sources could provide more comprehensive and new discoveries of DEGs. Here we introduce a new method, called rank scoring, to retrieve DEGs by integrating microarray and SAGE data and scoring each gene by their differentially expressed level in each dataset under consideration. As a proof of concept study, this method was applied to the discovery of DEGs of adenocarcinoma, a most critical subtype of lung cancer. Five microarray and six SAGE datasets of adenocarcinoma against normal lung tissues were analyzed using this method. Text mining and GO annotation were performed to validate the putative DEGs. The results showed excellent coincidence with previous studies and provided new hints. The results were also checked with permutation test and resample tests and showed high sensitivity and solid robustness.