A mixture model approach for the analysis of microarray gene expression data
Computational Statistics & Data Analysis
Cancer classification and prediction using logistic regression with Bayesian gene selection
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Link test-A statistical method for finding prostate cancer biomarkers
Computational Biology and Chemistry
Computational Biology and Chemistry
Gene modification identification under flux capacity uncertainty
Proceedings of the 50th Annual Design Automation Conference
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Identifying genes that are differentially expressed under different experimental conditions is a fundamental task in microarray studies. However, different ranking methods generate very different gene lists, and this could profoundly impact follow-up analyses and biological interpretation. Therefore, developing improved ranking methods are critical in microarray data analysis. We developed a new algorithm, the probabilistic fold change (PFC), which ranks genes based on a confidence interval estimate of fold change. We performed extensive testing using multiple benchmark data sources including the MicroArray Quality Control (MAQC) data sets. We corroborated our observations with MAQC data sets using qRT-PCR data sets and Latin square spike-in data sets. Along with PFC, we tested six other popular ranking algorithms including Mean Fold Change (FC), SAM, t-statistic (T), Bayesian-t (BAYT), Intensity-Conditional Fold Change (CFC), and Rank Product (RP). PFC achieved reproducibility and accuracy that are consistently among the best of the seven ranking algorithms while other ranking algorithms would show weakness in some cases. Contrary to common belief, our results demonstrated that statistical accuracy will not translate to biological reproducibility and therefore both quality aspects need to be evaluated.