Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

Sang Mee Lee; Baolin Wu; John H. Kersey

doi:10.1007/s12561-012-9076-3

Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

Sang Mee Lee, Baolin Wu, John H. Kersey

Biostatistics

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

1 Scopus citations

Abstract

In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.

Original language	English (US)
Pages (from-to)	38-54
Number of pages	17
Journal	Statistics in Biosciences
Volume	6
Issue number	1
DOIs	https://doi.org/10.1007/s12561-012-9076-3
State	Published - May 2014

Bibliographical note

Funding Information:
Acknowledgements This research was supported in part by a Biomedical Informatics and Computational Biology research grant from the University of Minnesota-Rochester, and National Institute of Health grant CA134848 and GM083345. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. We would like to thank the associate editor and two anonymous referees for their constructive comments, which have dramatically improved the presentation of the paper.

Keywords

EM
Finite mixture model
Gene set enrichment analysis

Publisher link

10.1007/s12561-012-9076-3

Find It

Find It at U of M Libraries

Cite this

@article{09d48a9ab22b43e4b6db7a0903d839c0,

title = "Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model",

abstract = "In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.",

keywords = "EM, Finite mixture model, Gene set enrichment analysis",

author = "Lee, {Sang Mee} and Baolin Wu and Kersey, {John H.}",

note = "Funding Information: Acknowledgements This research was supported in part by a Biomedical Informatics and Computational Biology research grant from the University of Minnesota-Rochester, and National Institute of Health grant CA134848 and GM083345. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. We would like to thank the associate editor and two anonymous referees for their constructive comments, which have dramatically improved the presentation of the paper.",

year = "2014",

month = may,

doi = "10.1007/s12561-012-9076-3",

language = "English (US)",

volume = "6",

pages = "38--54",

journal = "Statistics in Biosciences",

issn = "1867-1764",

publisher = "Springer New York",

number = "1",

}

TY - JOUR

T1 - Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

AU - Lee, Sang Mee

AU - Wu, Baolin

AU - Kersey, John H.

N1 - Funding Information: Acknowledgements This research was supported in part by a Biomedical Informatics and Computational Biology research grant from the University of Minnesota-Rochester, and National Institute of Health grant CA134848 and GM083345. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. We would like to thank the associate editor and two anonymous referees for their constructive comments, which have dramatically improved the presentation of the paper.

PY - 2014/5

Y1 - 2014/5

N2 - In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.

AB - In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.

KW - EM

KW - Finite mixture model

KW - Gene set enrichment analysis

UR - http://www.scopus.com/inward/record.url?scp=84899935181&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899935181&partnerID=8YFLogxK

U2 - 10.1007/s12561-012-9076-3

DO - 10.1007/s12561-012-9076-3

M3 - Article

AN - SCOPUS:84899935181

SN - 1867-1764

VL - 6

SP - 38

EP - 54

JO - Statistics in Biosciences

JF - Statistics in Biosciences

IS - 1

ER -

Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

Abstract

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this