Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data.
Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data.
复制标题
DOI：
10.1109/bibe.2008.4696797
复制
发表时间：
2008-10
期刊：
影响因子：
--
通讯作者：
Wang MD
中科院分区：
文献类型：
--
作者：
Siy PW;Moffitt RA;Parry RM;Chen Y;Liu Y;Sullards MC;Merrill AH Jr;Wang MD
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.
