Non-negative matrix factorization: Difference between revisions

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}}</ref> In the analysis of cancer mutations it has been used to identify common patterns of mutations that occur in many cancers and that probably have distinct causes.<ref>{{Cite journal|last1=Alexandrov|first1=Ludmil B.|last2=Nik-Zainal|first2=Serena|last3=Wedge|first3=David C.|last4=Campbell|first4=Peter J.|last5=Stratton|first5=Michael R.|date=2013-01-31|title=Deciphering signatures of mutational processes operative in human cancer|journal=Cell Reports|volume=3|issue=1|pages=246–259|doi=10.1016/j.celrep.2012.12.008|issn=2211-1247|pmc=3588146|pmid=23318258}}</ref> NMF techniques can identify sources of variation such as cell types, disease subtypes, population stratification, tissue composition, and tumor clonality.<ref>{{Cite journal|last1=Stein-O’Brien|first1=Genevieve L.|last2=Arora|first2=Raman|last3=Culhane|first3=Aedin C.|last4=Favorov|first4=Alexander V.|last5=Garmire|first5=Lana X.|last6=Greene|first6=Casey S.|last7=Goff|first7=Loyal A.|last8=Li|first8=Yifeng|last9=Ngom|first9=Aloune|last10=Ochs|first10=Michael F.|last11=Xu|first11=Yanxun|date=2018-10-01|title=Enter the Matrix: Factorization Uncovers Knowledge from Omics|url= |journal=Trends in Genetics|language=en|volume=34|issue=10|pages=790–805|doi=10.1016/j.tig.2018.07.003|issn=0168-9525|pmid=30143323|pmc=6309559}}</ref>

A particular variant of NMF, namely Non-Negative Matrix Tri-Factorization (NMTF),<ref>{{Cite journalbook

| last1 = Ding|last2 = Li|last3 = Peng|last4 = Park

| title =Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining | chapter=Orthogonal nonnegative matrix t-factorizations for clustering | date=2006 | year = 2006

| journal = Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

| year = 2006

| pages = 126&ndash;135

| doi = 10.1145/1150402.1150420