PIDEX : a Statistical Approach for Screening Differentially Expressed Genes Using Microarray Analysis

Microarray technology is being applied in pharmaceutical drug discovery. A typical experiment is conducted to compare the gene expression profiles under two different conditions and the purpose is to find genes differentially expressed under the conditions. Common practice is to use fold change for detecting differential expression. However, use of fold change can generate many false positive errors because of the existence of genes with low or undetectable expression levels. A novel method to analyze differentially expressed genes is presented that combines the fold change, change in the absolute intensity measurements and data reproducibility. It produces p-values for identifying differentially expressed genes (PIDEX). The proposed methodology is demonstrated by analyzing the expression profiling data from a public data set and an internally conducted experiment comparing two cell lines (ES2 and WI38). Results from these analyses and a validation study using quantitative RT-PCR assays suggest that PIDEX outperform the use of fold change alone. 1 Corresponding author. Global LG Biostatistics. Mail Stop D103B. Aventis Pharmaceuticals. Route 202-206. P.O.Box 6800. Bridgewater, NJ 08807-0800. The work was finished while the author was with Bristol-Myers Squibb. 2 Department of Applied Genomics. P.O. Box 5400. Pharmaceutical Research Institute. Bristol-Myers Squibb. Princeton, NJ 08543-5400. 3 Department of Statistis, University of Michigan, Ann Arbor, MI 48109-1285.