Determining gene expression on a single pair of microarrays.

TitleDetermining gene expression on a single pair of microarrays.
Publication TypeJournal Article
Year of Publication2008
AuthorsReid RW, Fodor AA
JournalBMC Bioinformatics
Date Published2008 Nov 21
KeywordsAlgorithms, Bayes Theorem, Databases, Genetic, Data Interpretation, Statistical, Gene Expression, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Reproducibility of Results, ROC Curve, Sensitivity and Specificity, Statistics, Nonparametric

<p><b>BACKGROUND: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>CONCLUSION: </b>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.</p>

Alternate JournalBMC Bioinformatics
PubMed ID19025600
PubMed Central IDPMC2605475