Abstract
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. 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 χ 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2391-2398 |
| Number of pages | 8 |
| Journal | Bioinformatics |
| Volume | 23 |
| Issue number | 18 |
| DOIs | |
| State | Published - Sep 15 2007 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Clinical Biochemistry
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
Selection and validation of normalization methods for c-DNA microarrays using within-array replications. / Fan, Jianqing; Niu, Yue.
In: Bioinformatics, Vol. 23, No. 18, 15.09.2007, p. 2391-2398.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Selection and validation of normalization methods for c-DNA microarrays using within-array replications
AU - Fan, Jianqing
AU - Niu, Yue
PY - 2007/9/15
Y1 - 2007/9/15
N2 - 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. 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 χ 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.
AB - 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. 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 χ 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.
UR - http://www.scopus.com/inward/record.url?scp=34548761943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548761943&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btm361
DO - 10.1093/bioinformatics/btm361
M3 - Article
C2 - 17660210
AN - SCOPUS:34548761943
VL - 23
SP - 2391
EP - 2398
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 18
ER -