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	A quick and effective multivariate statistical strategy for imaging mass spectrometry
	Fei Tang ; Yi Chen ; Tie-Gang Li ; Jiu-Ming He ; Zeper Abliz ; Gang Huang ; Xiao-Hao Wang ; Fei Tang ; Yi Chen ; Tie-Gang Li ; Jiu-Ming He ; Zeper Abliz ; Gang Huang ; Xiao-Hao Wang
	2016-03-30 ; 2016-03-30
关键词	Imaging mass spectrometry Multivariate statistical analysis Potential biomarkers Quick and effective O657.63
其他题名	A quick and effective multivariate statistical strategy for imaging mass spectrometry
中文摘要	A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry(IMS) techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS(AFAI-IMS).The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis(PCA) and partial least square analysis(PLS).It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.; A new multivariate statistical strategy for analyzing large datasets that are produced by imaging mass spectrometry(IMS) techniques is reported.The strategy divides the whole datacube of the sample into several subsets and analyses them one by one to obtain the results.Instead of analyzing the whole datacube at one time,the strategy makes the analysis easier and decreases the computation time greatly.In this report,the IMS data are produced by the air flow-assisted ionization IMS(AFAI-IMS).The strategy can be used in combination with most multivariate statistical analysis methods.In this paper,the strategy was combined with the principal component analysis(PCA) and partial least square analysis(PLS).It was proven to be effective by analyzing the handwriting sample.By using the strategy,the m/z corresponding to the specific lipids in rat brain tissue were distinguished successfully.Moreover the analysis time grew linearly instead of exponentially as the size of sample increased.The strategy developed in this study has enormous potential for searching for the mjz of potential biomarkers quickly and effectively.
语种	英语 ; 英语
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/144611]
专题	清华大学
推荐引用方式 GB/T 7714	Fei Tang,Yi Chen,Tie-Gang Li,et al. A quick and effective multivariate statistical strategy for imaging mass spectrometry[J],2016, 2016.
APA	Fei Tang.,Yi Chen.,Tie-Gang Li.,Jiu-Ming He.,Zeper Abliz.,...&Xiao-Hao Wang.(2016).A quick and effective multivariate statistical strategy for imaging mass spectrometry..
MLA	Fei Tang,et al."A quick and effective multivariate statistical strategy for imaging mass spectrometry".(2016).

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