Model selection and efficiency testing for normalization of cDNA microarray data.
In this study we present two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an improved ability to remove systematic errors and to reduce variability in microarray data. The analysis also reveals that without parameter optimization local regression is frequently insufficient to remove systematic errors in microarray data.
Institute for Theoretical Biology, Humboldt-Universität, Invalidenstrasse 43, 10115 Berlin, Germany. m.futschik@biologie.hu-berlin.de
Matthias
M
Futschik
Toni
T
Crompton
D000465:Algorithms; D015982:Bias; D045744:Cell Line, Tumor; D003110:Colonic Neoplasms / Q000235:genetics / Q000473:pathology; D004396:Coloring Agents; D018076:DNA, Complementary / Q000235:genetics*; D005347:Fibroblasts / Q000187:drug effects / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods / Q000592:standards; D006801:Humans; D008207:Lymphatic Metastasis / Q000235:genetics; D008297:Male; D008875:Middle Aged; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards*; D012333:RNA, Messenger / Q000032:analysis / Q000235:genetics; D012015:Reference Standards; D015203:Reproducibility of Results; D012107:Research Design
D004396:Coloring Agents; D018076:DNA, Complementary; D012333:RNA, Messenger