{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:12:53Z","timestamp":1761559973294,"version":"3.33.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Normalization of microarray data is essential for multiple-array analyses. Several normalization protocols have been proposed based on different biological or statistical assumptions. A fundamental problem arises whether they have effectively normalized arrays. In addition, for a given array, the question arises how to choose a method to most effectively normalize the microarray data.<\/jats:p><jats:p>Results: We propose several techniques to compare the effectiveness of different normalization methods. We approach the problem by constructing statistics to test whether there are any systematic biases in the expression profiles among duplicated spots within an array. The test statistics involve estimating the genewise variances. This is accomplished by using several novel methods, including empirical Bayes methods for moderating the genewise variances and the smoothing methods for aggregating variance information. P-values are estimated based on a normal or \u03c7 approximation. With estimated P-values, we can choose a most appropriate method to normalize a specific array and assess the extent to which the systematic biases due to the variations of experimental conditions have been removed. The effectiveness and validity of the proposed methods are convincingly illustrated by a carefully designed simulation study. The method is further illustrated by an application to human placenta cDNAs comprising a large number of clones with replications, a customized microarray experiment carrying just a few hundred genes on the study of the molecular roles of Interferons on tumor, and the Agilent microarrays carrying tens of thousands of total RNA samples in the MAQC project on the study of reproducibility, sensitivity and specificity of the data.<\/jats:p><jats:p>Availability: Code to implement the method in the statistical package R is available from the authors.<\/jats:p><jats:p>Contact: \u00a0jqfan@princeton.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm361","type":"journal-article","created":{"date-parts":[[2007,7,28]],"date-time":"2007-07-28T00:29:50Z","timestamp":1185582590000},"page":"2391-2398","source":"Crossref","is-referenced-by-count":17,"title":["Selection and validation of normalization methods for c-DNA microarrays using within-array replications"],"prefix":"10.1093","volume":"23","author":[{"given":"Jianqing","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Operations Research and Financial Engineering Princeton University, Princeton, NJ 08544, USA"}]},{"given":"Yue","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Operations Research and Financial Engineering Princeton University, Princeton, NJ 08544, USA"}]}],"member":"286","published-online":{"date-parts":[[2007,7,27]]},"reference":[{"key":"2023041106230652100_","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1093\/biostatistics\/kxh018","article-title":"Improved statistical tests for differential gene expression by shrinking variance components estimates","volume":"6","author":"Cui","year":"2005","journal-title":"Biostatistics"},{"key":"2023041106230652100_","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1093\/biostatistics\/kxj038","article-title":"A new approach to intensity-dependent normalization of two-channel microarrays","volume":"8","author":"Dabney","year":"2007","journal-title":"Biostatistics"},{"key":"2023041106230652100_","first-page":"8","article-title":"Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships","author":"Dabney","year":"2007","journal-title":"Genome Biol"},{"key":"2023041106230652100_","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J. 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