Analysis of Variance for Gene Expression Microarray Data Export

@article{citeulike:963600, abstract = {Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.}, address = {The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.}, author = {Kerr, M. Kathleen and Martin, Mitchell and Churchill, Gary A.}, citeulike-article-id = {963600}, citeulike-linkout-0 = {http://dx.doi.org/10.1089/10665270050514954}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/11382364}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=11382364}, doi = {10.1089/10665270050514954}, issn = {1066-5277}, journal = {Journal of Computational Biology}, keywords = {00}, month = {December}, number = {6}, pages = {819--837}, posted-at = {2008-07-21 12:16:10}, priority = {2}, title = {Analysis of Variance for Gene Expression Microarray Data}, url = {http://dx.doi.org/10.1089/10665270050514954}, volume = {7}, year = {2000} }

TY - JOUR ID - citeulike:963600 L3 - citeulike-article-id:963600 N2 - Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data. IS - 6 JF - Journal of Computational Biology SN - 1066-5277 AD - The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA. EP - 837 TI - Analysis of Variance for Gene Expression Microarray Data VL - 7 SP - 819 KW - 00 AU - Kerr, Kathleen AU - Martin, Mitchell AU - Churchill, Gary PY - 2000/12// UR - http://dx.doi.org/10.1089/10665270050514954 ER -