Siy Peter W, Moffitt Richard A, Parry R Mitchell, Chen Yanfeng, Liu Ying, Sullards M Cameron, Merrill Alfred H, Wang May D
School of Electrical and Computer Engineering, Georgia Tech, Atlanta, GA 30332 USA (
Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA USA (
Proc IEEE Int Symp Bioinformatics Bioeng. 2008 Oct;2008. doi: 10.1109/BIBE.2008.4696797. Epub 2008 Dec 8.
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.
成像质谱法是一种用于了解二维样品中分子分布的方法。该方法对多种分子有效，但会产生大量数据。手动从这些大型数据集中提取重要信息很困难，因此需要用于发现重要空间和光谱特征的自动化方法。本文解释并探讨了独立成分分析和非负矩阵分解，将其作为识别数据中潜在因素的工具。将这些技术与更标准的分析工具主成分分析进行了比较和对比。发现独立成分分析和非负矩阵分解是更有效的分析方法。使用小鼠小脑数据集进行测试。
