Disease Prediction Power and Stability of Differential Expressed Genes

Selecting feature genes for disease prediction is one of the most important applications of microarray technology. However, gene lists obtained in different studies for a same clinical type of patients often differ widely and have few genes in common. Recent researches suggest that gene lists ranked by fold change are more reproducible than by t-test. Here, based on the resampling method, we use training sets of different sizes to select features as top-ranked by P- value of t-test, d-value of SAM, and fold change. Then, we evaluate the stability and the disease classification power of each top ranked gene list. Our result suggests that for disease classification, gene lists selected through d-value ranking are most suitable concerning both reproducibility and classification power.