Determining gene expression on a single pair of microarrays

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.PINC outperforms previously published methods for determining differentially expressed genes when comparing Affymetrix microarrays with N = 1 in each condition. When applied to biological samples, PINC can be used to assess the degree of variability observed among biological replicates in addition to analyzing isolated pairs of microarrays.Numerous strategies have been devised to come up with rigorous methods for detecting differentially expressed genes in sets of microarray data (for review see[1]). The majority of microarray analytical methods require N = 3 in each condition in order to perform statistical measures. Due to the expense of microarrays, experimental imperfections such as poor hybridization and limited quantities of available biological sample source, it is not always possible to obtain the required sample sizes.On an Affymetrix expression array, such as the HG-U133A GeneChip, each gene is represented on the array by a number of separate 25 mer probes that correspond to a part of the gene sequence. Many popular statistical methods including MAS5[2], RMA[3] and GCRMA[4] summarize probes into a single value for the entire probeset before performing statistical inference. In contrast, probe-level modeling has been used by the software packages affyPLM[5] for quality-control purposes. There are also a number of statistical models that directly utilize probe information in statistical inference including Logit-T [6], Fisher's combined p-value [