Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.
Identifieur interne : 003652 ( PubMed/Corpus ); précédent : 003651; suivant : 003653Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.
Auteurs : Saumyadipta Pyne ; Sharon X. Lee ; Kui Wang ; Jonathan Irish ; Pablo Tamayo ; Marc-Danie Nazaire ; Tarn Duong ; Shu-Kay Ng ; David Hafler ; Ronald Levy ; Garry P. Nolan ; Jill Mesirov ; Geoffrey J. MclachlanSource :
- PloS one [ 1932-6203 ] ; 2014.
English descriptors
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- MESH :
Abstract
In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.
DOI: 10.1371/journal.pone.0100334
PubMed: 24983991
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Lee</name><affiliation><nlm:affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Kui" sort="Wang, Kui" uniqKey="Wang K" first="Kui" last="Wang">Kui Wang</name><affiliation><nlm:affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Irish, Jonathan" sort="Irish, Jonathan" uniqKey="Irish J" first="Jonathan" last="Irish">Jonathan Irish</name><affiliation><nlm:affiliation>Division of Oncology, Stanford Medical School, Stanford, California, United States of America; Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America; Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Tamayo, Pablo" sort="Tamayo, Pablo" uniqKey="Tamayo P" first="Pablo" last="Tamayo">Pablo Tamayo</name><affiliation><nlm:affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Nazaire, Marc Danie" sort="Nazaire, Marc Danie" uniqKey="Nazaire M" first="Marc-Danie" last="Nazaire">Marc-Danie Nazaire</name><affiliation><nlm:affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Duong, Tarn" sort="Duong, Tarn" uniqKey="Duong T" first="Tarn" last="Duong">Tarn Duong</name><affiliation><nlm:affiliation>Molecular Mechanisms of Intracellular Transport, Unit Mixte de Recherche 144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France.</nlm:affiliation></affiliation></author><author><name sortKey="Ng, Shu Kay" sort="Ng, Shu Kay" uniqKey="Ng S" first="Shu-Kay" last="Ng">Shu-Kay Ng</name><affiliation><nlm:affiliation>School of Medicine, Griffith University, Meadowbrook, Queensland, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Hafler, David" sort="Hafler, David" uniqKey="Hafler D" first="David" last="Hafler">David Hafler</name><affiliation><nlm:affiliation>Department of Neurology, Yale School of Medicine, New Haven, Connecticut, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Levy, Ronald" sort="Levy, Ronald" uniqKey="Levy R" first="Ronald" last="Levy">Ronald Levy</name><affiliation><nlm:affiliation>Division of Oncology, Stanford Medical School, Stanford, California, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Nolan, Garry P" sort="Nolan, Garry P" uniqKey="Nolan G" first="Garry P" last="Nolan">Garry P. Nolan</name><affiliation><nlm:affiliation>Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Mesirov, Jill" sort="Mesirov, Jill" uniqKey="Mesirov J" first="Jill" last="Mesirov">Jill Mesirov</name><affiliation><nlm:affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Mclachlan, Geoffrey J" sort="Mclachlan, Geoffrey J" uniqKey="Mclachlan G" first="Geoffrey J" last="Mclachlan">Geoffrey J. Mclachlan</name><affiliation><nlm:affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Computational Biology (methods)</term><term>Computer Simulation</term><term>Flow Cytometry</term><term>Humans</term><term>Software</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Computer Simulation</term><term>Flow Cytometry</term><term>Humans</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24983991</PMID><DateCreated><Year>2014</Year><Month>07</Month><Day>02</Day></DateCreated><DateCompleted><Year>2016</Year><Month>01</Month><Day>12</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>7</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.</ArticleTitle><Pagination><MedlinePgn>e100334</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0100334</ELocationID><Abstract><AbstractText>In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Pyne</LastName><ForeName>Saumyadipta</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, Hyderabad, Andhra Pradesh, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Sharon X</ForeName><Initials>SX</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Kui</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Irish</LastName><ForeName>Jonathan</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Division of Oncology, Stanford Medical School, Stanford, California, United States of America; Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America; Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tamayo</LastName><ForeName>Pablo</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nazaire</LastName><ForeName>Marc-Danie</ForeName><Initials>MD</Initials><AffiliationInfo><Affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Duong</LastName><ForeName>Tarn</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Molecular Mechanisms of Intracellular Transport, Unit Mixte de Recherche 144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ng</LastName><ForeName>Shu-Kay</ForeName><Initials>SK</Initials><AffiliationInfo><Affiliation>School of Medicine, Griffith University, Meadowbrook, Queensland, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hafler</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Neurology, Yale School of Medicine, New Haven, Connecticut, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Levy</LastName><ForeName>Ronald</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Division of Oncology, Stanford Medical School, Stanford, California, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nolan</LastName><ForeName>Garry P</ForeName><Initials>GP</Initials><AffiliationInfo><Affiliation>Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mesirov</LastName><ForeName>Jill</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>McLachlan</LastName><ForeName>Geoffrey J</ForeName><Initials>GJ</Initials><AffiliationInfo><Affiliation>Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>K99 CA143231</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R00 CA143231</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>07</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections 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