Determining gene expression on a single pair of microarrays

Background: In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments. Results: PINC treats each pair of probes within a probeset as an independent measure of gene expression using the Bayesian framework of the Cyber-T algorithm and then assigns a corrected p-value for each gene comparison. The p-values generated by PINC accurately control False Discovery rate on Affymetrix control data sets, but are small enough that family-wise error rates (such as the Holm's step down method) can be used as a conservative alternative to false discovery rate with little loss of sensitivity on control data sets. Conclusion: PINC outperforms previously publis...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Robert W. Reid, Anthony A. Fodor
• Keywords: Affymetrix Microarrays, BMCBI 2008, Expressed Genes, False Discovery Rate