A statistical framework for expression-based molecular classification in cancer

被引：88

作者：

Parmigiani, G ^{[1
]}

Garrett, ES ^{[1
]}

Anbazhagan, R ^{[1
]}

Gabrielson, E ^{[1
]}

机构：

[1] Johns Hopkins Univ, Sch Med, Div Oncol & Baltimore, Baltimore, MD 21205 USA

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | 2002年 / 64卷

关键词：

microarray data analysis; mixture distributions; molecular classification of cancer;

D O I：

10.1111/1467-9868.00358

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.

引用

页码：717 / 736

页数：20

共 36 条

[21] Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations [J].

Lee, MLT ;

Kuo, FC ;

Whitmore, GA ;

Sklar, J .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2000, 97 (18) :9834-9839

[22] A mixture model-based approach to the clustering of microarray expression data [J].

McLachlan, GJ ;

Bean, RW ;

Peel, D .

BIOINFORMATICS, 2002, 18 (03) :413-422

[23] On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data [J].

Newton, MA ;

Kendziorski, CM ;

Richmond, CS ;

Blattner, FR ;

Tsui, KW .

JOURNAL OF COMPUTATIONAL BIOLOGY, 2001, 8 (01) :37-52

[24] Distinctive gene expression patterns in human mammary epithelial cells and breast cancers [J].

Perou, CM ;

Jeffrey, SS ;

Van de Rijn, M ;

Rees, CA ;

Eisen, MB ;

Ross, DT ;

Pergamenschikov, A ;

Williams, CF ;

Zhu, SX ;

Lee, JCF ;

Lashkari, D ;

Shalon, D ;

Brown, PO ;

Botstein, D .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1999, 96 (16) :9212-9217

[25] Computational analysis of microarray data [J].

Quackenbush, J .

NATURE REVIEWS GENETICS, 2001, 2 (06) :418-427

[26]

Ripley B. D., 1996, Pattern Recognition and Neural Networks

[27]

ROUSSEEUW P, 1996, J STAT SFTWR, V1, P1

[28] Navigating gene expression using microarrays - a technology review [J].

Schulze, A ;

Downward, J .

NATURE CELL BIOLOGY, 2001, 3 (08) :E190-E195

[29]

Segal E, 2001, Bioinformatics, V17 Suppl 1, pS243

[30]

Tavassoli FAS., 1992, Pathology of the breast

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