Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data

被引：0

作者：

Siy, Peter W. ^{[1
]}

Moffitt, Richard A. ^{[2
,3
]}

Parry, R. Mitchell ^{[2
,3
]}

Chen, Yanfeng ^{[4
]}

Liu, Ying ^{[5
]}

Sullards, M. Cameron ^{[4
,5
]}

Merrill, Alfred H., Jr. ^{[4
,5
,6
]}

Wang, May D. ^{[1
,2
,3
,6
]}

机构：

[1] Georgia Tech, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

[2] Georgia Tech, Dept Biomed Engn, Atlanta, GA 30332 USA

[3] Emory Univ, Atlanta, GA USA

[4] Georgia Tech, Sch Chem & Biochem, Atlanta, GA USA

[5] Georgia Tech, Sch Biol, Atlanta, GA USA

[6] Georgia Tech, Petit Inst Bioengn & Biosci, Atlanta, GA USA

来源：

8TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING, VOLS 1 AND 2 | 2008年

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

Imaging Mass Spectrometry; Independent Component Analysis; Non-negative Matrix Factorization; Principle Component Analysis;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Imaging mass spectrometry is a method for understanding the molecular distribution in a two-dimensional sample. This method is effective for a wide range of molecules, but generates a large amount of data. It is difficult to extract important information from these large datasets manually and automated methods for discovering important spatial and spectral features are needed. Independent component analysis and non-negative matrix factorization are explained and explored as tools for identifying underlying factors in the data. These techniques are compared and contrasted with principle component analysis, the more standard analysis tool. Independent component analysis and non-negative matrix factorization are found to be more effective analysis methods. A mouse cerebellum dataset is used for testing.

引用

页码：875 / +

页数：2

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