Application of a correlation correction factor in a microarray cross-platform reproducibility study

Background Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations. Results In this paper, three technical replicate microarrays were hybridized to each of three platforms. The three platforms were then analyzed to assess both intra- and cross-platform reproducibility. We present various methods for examining intra-platform reproducibility. We also examine cross-platform reproducibility using Pearson\u27s correlation. Additionally, we previously developed a correction factor for Pearson\u27s correlation which is applicable when X and Y are measured with error. Herein we demonstrate that correcting for measurement error by estimating the disattenuated correlation substantially improves cross-platform correlations. Conclusion When estimating cross-platform correlation, it is essential to thoroughly evaluate intra-platform reproducibility as a first step. In addition, since measurement error is present in microarray gene expression data, methods to correct for attenuation are useful in decreasing the bias in cross-platform correlation estimates

Genes Involved in Radiation Therapy Response in Head and Neck Cancers

OBJECTIVES: This is a pilot study designed to identify gene expression profiles able to stratify head and neck squamous cell carcinoma (HNSCC) tumors that may or may not respond to chemoradiation or radiation therapy. STUDY DESIGN: We prospectively evaluated 14 HNSCC specimens, arising from patients undergoing chemoradiotherapy or radiotherapy alone with curative intent. A complete response was assessed by clinical evaluation with no evidence of gross tumor after a 2-year follow-up period. METHODS: Residual biopsy samples from eight complete responders (CR) and six nonresponders (NR) were evaluated by genome-wide gene expression profiling using HG-U133A 2.0 arrays. Univariate parametric t-tests with proportion of false discoveries controlled by multivariate permutation tests were used to identify genes with significantly different gene expression levels between CR and NR cases. Six different prediction algorithms were used to build gene predictor lists. Three representative genes showing 100% crossvalidation support after leave-one-out crossvalidation (LOOCV) were further validated using real-time QRT-PCR. RESULTS: We identified 167 significant probe sets that discriminate between the two classes, which were used to build gene predictor lists. Thus, 142 probe sets showed an accuracy of prediction ranging from 93% to 100% across all six prediction algorithms. The genes represented by these 142 probe sets were further classified into different functional networks that included cellular development, cellular movement, and cancer. CONCLUSIONS: The results presented herein offer encouraging preliminary data that may provide a basis for a more precise prognosis of HNSCC, as well as a molecular-based therapy decision for the management of these cancers

Application of a correlation correction factor in a microarray cross-platform reproducibility study-1

<p><b>Copyright information:</b></p><p>Taken from "Application of a correlation correction factor in a microarray cross-platform reproducibility study"</p><p>http://www.biomedcentral.com/1471-2105/8/447</p><p>BMC Bioinformatics 2007;8():447-447.</p><p>Published online 15 Nov 2007</p><p>PMCID:PMC2211756.</p><p></p>n among the three platforms

Application of a correlation correction factor in a microarray cross-platform reproducibility study-0

<p><b>Copyright information:</b></p><p>Taken from "Application of a correlation correction factor in a microarray cross-platform reproducibility study"</p><p>http://www.biomedcentral.com/1471-2105/8/447</p><p>BMC Bioinformatics 2007;8():447-447.</p><p>Published online 15 Nov 2007</p><p>PMCID:PMC2211756.</p><p></p> to the 1,288 genes in common among the three platforms