Determining gene expression on a single pair of microarrays Export

@article{citeulike:3657405, abstract = {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 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.}, author = {Reid, Robert and Fodor, Anthony}, citeulike-article-id = {3657405}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-489}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19025600}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19025600}, doi = {10.1186/1471-2105-9-489}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {deg, expressiondata}, number = {1}, posted-at = {2008-11-23 09:37:30}, priority = {2}, title = {Determining gene expression on a single pair of microarrays}, url = {http://dx.doi.org/10.1186/1471-2105-9-489}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:3657405 L3 - citeulike-article-id:3657405 N2 - 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 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. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Determining gene expression on a single pair of microarrays VL - 9 KW - deg KW - expressiondata AU - Reid, Robert AU - Fodor, Anthony PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-489 ER -