A mixture model approach for the analysis of microarray gene expression data

David B. Allison; Gary L. Gadbury; Moonseong Heo; José R. Fernández; Cheol Koo Lee; Cheol Koo Lee; Tomas A. Prolla; Richard Weindruch; Richard Weindruch

doi:10.1016/s0167-9473(01)00046-9

A mixture model approach for the analysis of microarray gene expression data

David B. Allison, Gary L. Gadbury, Moonseong Heo, José R. Fernández, Cheol Koo Lee, Cheol Koo Lee, Tomas A. Prolla, Richard Weindruch, Richard Weindruch

Research output: Contribution to journal › Article › peer-review

273 Citations (Scopus)

Abstract

Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice.

Original language	English
Pages (from-to)	1-20
Number of pages	20
Journal	Computational Statistics and Data Analysis
Volume	38
Issue number	5
DOIs	https://doi.org/10.1016/s0167-9473(01)00046-9
Publication status	Published - 2002 Mar 28
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/s0167-9473(01)00046-9

Cite this

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title = "A mixture model approach for the analysis of microarray gene expression data",

abstract = "Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice.",

author = "Allison, {David B.} and Gadbury, {Gary L.} and Moonseong Heo and Fern{\'a}ndez, {Jos{\'e} R.} and Lee, {Cheol Koo} and Lee, {Cheol Koo} and Prolla, {Tomas A.} and Richard Weindruch and Richard Weindruch",

note = "Funding Information: This research was supported in part by NIH grants R29DK47256, R01DK51716, P30DK26687, P01AG11915, R01ES09912 and 5 U24 DK58776. We are grateful to Drs. Nicholas Schork, Joel Horowitz, and Michael C. Neale for their helpful comments. ",

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T1 - A mixture model approach for the analysis of microarray gene expression data

AU - Allison, David B.

AU - Gadbury, Gary L.

AU - Heo, Moonseong

AU - Fernández, José R.

AU - Lee, Cheol Koo

AU - Prolla, Tomas A.

AU - Weindruch, Richard

N1 - Funding Information: This research was supported in part by NIH grants R29DK47256, R01DK51716, P30DK26687, P01AG11915, R01ES09912 and 5 U24 DK58776. We are grateful to Drs. Nicholas Schork, Joel Horowitz, and Michael C. Neale for their helpful comments.

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Y1 - 2002/3/28

N2 - Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice.

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A mixture model approach for the analysis of microarray gene expression data

Abstract

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