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Statistical Designs for Two‐Color Spotted Microarray Experiments
Author(s) -
Chai FengShun,
Liao ChenTuo,
Tsai ShinFu
Publication year - 2007
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200510270
Subject(s) - normalization (sociology) , dna microarray , computer science , expression (computer science) , heuristic , optimal design , algorithm , data mining , design of experiments , gene chip analysis , mathematics , statistics , biology , artificial intelligence , gene expression , gene , machine learning , genetics , sociology , anthropology , programming language
Two‐color cDNA or oligonucleotide‐based spotted microarrays have been commonly used in measuring the expression levels of thousands of genes simultaneously. To realize the immense potential of this powerful new technology, budgeted within limited resources or other constraints, practical designs with high efficiencies are in demand. In this study, we address the design issue concerning the arrangement of the mRNA samples labeled with fluorescent dyes and hybridized on the slides. A normalization model is proposed to characterize major sources of systematic variation in a two‐color microarray experiment. This normalization model establishes a connection between designs for two‐color microarray experiments with a particular class of classical row‐column designs. A heuristic algorithm for constructing A‐optimal or highly efficient designs is provided. Statistical optimality results are found for some of the designs generated from the algorithm. It is believed that the constructed designs are the best or very close to the best possible for estimating the relative gene expression levels among the mRNA samples of interest. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)