The Estimation of Dimensionality In Gene Expression Data using Nonnegative Matrix Factorization

被引：0

作者：

Kelton, Conor J. ^{[1
]}

Lee, Waishing ^{[2
]}

Rusay, Matthew ^{[1
]}

Maxian, Ondrej ^{[1
]}

Fertig, Elana J. ^{[2
]}

Ochs, Michael F. ^{[1
]}

机构：

[1] Coll New Jersey, Dept Math & Stat, Ewing, NJ 08628 USA

[2] Johns Hopkins Sch Med, Div Oncol Biostat & Bioinformat, Sidney Kimmel Comprehens Canc Ctr, Baltimore, MD 21205 USA

来源：

PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE | 2015年

关键词：

MICROARRAY DATA; DECOMPOSITION; DISCOVERY; PROFILES;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Nonnegative matrix factorization and other decomposition methods have proven to offer significant advantages for the interpretation of genome-wide gene expression data. However, unlike analytic methods, they suffer from instability in the inferred factors or patterns as the dimensionality is changed. We present here two statistics, one mathematical and one biological, that estimate the dimensionality. We show that they provide close though not identical estimates, and that they provide strong evidence for elimination of some potential factorizations.

引用

页码：1642 / 1649

页数：8

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