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Assessing stability of gene selection in microarray data analysis.

作者信息

Qiu Xing, Xiao Yuanhui, Gordon Alexander, Yakovlev Andrei

机构信息

Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA.

出版信息

BMC Bioinformatics. 2006 Feb 1;7:50. doi: 10.1186/1471-2105-7-50.


DOI:10.1186/1471-2105-7-50
PMID:16451725
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1403808/
Abstract

BACKGROUND: The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data. RESULTS: Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations. CONCLUSION: Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/19e070a2fe2b/1471-2105-7-50-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/bb48c085f091/1471-2105-7-50-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/e9585e3f75fa/1471-2105-7-50-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/6aaecc06a463/1471-2105-7-50-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/ca08ecfaebbb/1471-2105-7-50-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/d2e371b6df0b/1471-2105-7-50-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/2d8412d92b90/1471-2105-7-50-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/19e070a2fe2b/1471-2105-7-50-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/bb48c085f091/1471-2105-7-50-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/e9585e3f75fa/1471-2105-7-50-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/6aaecc06a463/1471-2105-7-50-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/ca08ecfaebbb/1471-2105-7-50-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/d2e371b6df0b/1471-2105-7-50-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/2d8412d92b90/1471-2105-7-50-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6e/1403808/19e070a2fe2b/1471-2105-7-50-7.jpg

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本文引用的文献

[1]
Correlation between gene expression levels and limitations of the empirical bayes methodology for finding differentially expressed genes.

Stat Appl Genet Mol Biol. 2005

[2]
The effects of normalization on the correlation structure of microarray data.

BMC Bioinformatics. 2005-5-16

[3]
Use of within-array replicate spots for assessing differential expression in microarray experiments.

Bioinformatics. 2005-5-1

[4]
The effect of replication on gene expression microarray experiments.

Bioinformatics. 2003-9-1

[5]
Statistical significance for genomewide studies.

Proc Natl Acad Sci U S A. 2003-8-5

[6]
Gene selection in microarray data: the elephant, the blind men and our algorithms.

Curr Opin Struct Biol. 2003-6

[7]
Identifying differentially expressed genes using false discovery rate controlling procedures.

Bioinformatics. 2003-2-12

[8]
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Bioinformatics. 2003-1-22

[9]
A bootstrap resampling procedure for model building: application to the Cox regression model.

Stat Med. 1992-12

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