Probability fold change: A robust computational approach for identifying differentially expressed gene lists
作者： Xutao Deng , Jun Xu , James Hui , Charles Wang
DOI: 10.1016/J.CMPB.2008.07.013
关键词:
摘要: Identifying genes that are differentially expressed under different experimental conditions is a fundamental task in microarray studies. However, ranking methods generate very gene lists, and this could profoundly impact follow-up analyses biological interpretation. Therefore, developing improved critical data analysis. We developed new algorithm, the probabilistic fold change (PFC), which ranks based on confidence interval estimate of change. performed extensive testing using multiple benchmark sources including MicroArray Quality Control (MAQC) sets. corroborated our observations with MAQC sets qRT-PCR Latin square spike-in Along PFC, we tested six other popular algorithms Mean Fold Change (FC), SAM, t-statistic (T), Bayesian-t (BAYT), Intensity-Conditional (CFC), Rank Product (RP). PFC achieved reproducibility accuracy consistently among best seven while would show weakness some cases. Contrary to common belief, results demonstrated statistical will not translate therefore both quality aspects need be evaluated.
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