Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Export

@article{citeulike:166213, abstract = {There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.}, address = {Department of Statistics, Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720-3860, USA.}, author = {Yang, Y. H. and Dudoit, S. and Luu, P. and Lin, D. M. and Peng, V. and Ngai, J. and Speed, T. P.}, citeulike-article-id = {166213}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/nar/30.4.e15}, citeulike-linkout-1 = {http://nar.oxfordjournals.org/cgi/content/abstract/30/4/e15}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/11842121}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=11842121}, day = {15}, doi = {10.1093/nar/30.4.e15}, issn = {1362-4962}, journal = {Nucleic Acids Res}, keywords = {puces\_adn, stat}, month = {February}, number = {4}, posted-at = {2005-09-18 07:20:57}, priority = {0}, title = {Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.}, url = {http://dx.doi.org/10.1093/nar/30.4.e15}, volume = {30}, year = {2002} }

TY - JOUR ID - citeulike:166213 L3 - citeulike-article-id:166213 N2 - There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides. IS - 4 JF - Nucleic Acids Res SN - 1362-4962 AD - Department of Statistics, Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720-3860, USA. TI - Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. VL - 30 KW - puces_adn KW - stat AU - Yang, YH AU - Dudoit, S AU - Luu, P AU - Lin, DM AU - Peng, V AU - Ngai, J AU - Speed, TP PY - 2002/02/15/ UR - http://dx.doi.org/10.1093/nar/30.4.e15 ER -