Peter W Siy, Richard A Moffitt, R Mitchell Parry, Yanfeng Chen, Ying Liu, M Cameron Sullards, Alfred H Merrill, May D Wang
{"title":"用于分析成像质谱数据的矩阵因式分解技术。","authors":"Peter W Siy, Richard A Moffitt, R Mitchell Parry, Yanfeng Chen, Ying Liu, M Cameron Sullards, Alfred H Merrill, May D Wang","doi":"10.1109/BIBE.2008.4696797","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382992/pdf/nihms805862.pdf","citationCount":"0","resultStr":"{\"title\":\"Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data.\",\"authors\":\"Peter W Siy, Richard A Moffitt, R Mitchell Parry, Yanfeng Chen, Ying Liu, M Cameron Sullards, Alfred H Merrill, May D Wang\",\"doi\":\"10.1109/BIBE.2008.4696797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":87347,\"journal\":{\"name\":\"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382992/pdf/nihms805862.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2008.4696797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2008/12/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2008.4696797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2008/12/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Matrix Factorization Techniques for Analysis of Imaging Mass Spectrometry Data.
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.