Joint and individual variation explained (jive) for integrated analysis of multiple data types
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TLDR
JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure.Abstract:
Research in several fields now requires the analysis of datasets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types.read more
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Journal ArticleDOI
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Andrea Cossarizza,Hyun-Dong Chang,Andreas Radbruch,Andreas Acs,Dieter Adam,Sabine Adam-Klages,William W. Agace,William W. Agace,Nima Aghaeepour,Mübeccel Akdis,Matthieu Allez,Larissa Nogueira Almeida,Giorgia Alvisi,Graham Anderson,Immanuel Andrä,Francesco Annunziato,Achille Anselmo,Petra Bacher,Cosima T. Baldari,Sudipto Bari,Vincenzo Barnaba,Vincenzo Barnaba,Joana Barros-Martins,Luca Battistini,Wolfgang Bauer,Sabine Baumgart,Nicole Baumgarth,Dirk Baumjohann,Bianka Baying,Mary Bebawy,Burkhard Becher,Wolfgang Beisker,Vladimir Benes,Rudi Beyaert,Alfonso Blanco,Dominic A. Boardman,Christian Bogdan,Jessica G. Borger,Giovanna Borsellino,Philip E. Boulais,Jolene Bradford,Dirk Brenner,Dirk Brenner,Ryan R. Brinkman,Anna E. S. Brooks,Dirk H. Busch,Martin Büscher,Timothy P. Bushnell,Federica Calzetti,Garth Cameron,Ilenia Cammarata,Xuetao Cao,Susanna Cardell,Stefano Casola,Marco A. Cassatella,Andrea Cavani,Antonio Celada,Lucienne Chatenoud,Pratip K. Chattopadhyay,Sue Chow,Eleni Christakou,Eleni Christakou,Luka Cicin-Sain,Mario Clerici,Federico Colombo,Laura Cook,Anne Cooke,Andrea M. Cooper,Alexandra J. Corbett,Antonio Cosma,Lorenzo Cosmi,Pierre Coulie,Ana Cumano,Ljiljana Cvetkovic,Van Duc Dang,Chantip Dang-Heine,Martin S. Davey,Derek Davies,Sara De Biasi,Genny Del Zotto,Gelo Victoriano Dela Cruz,Michael Delacher,Silvia Della Bella,Paolo Dellabona,Günnur Deniz,Mark C. Dessing,James P. Di Santo,Andreas Diefenbach,Francesco Dieli,Andreas Dolf,Thomas Dörner,Regine J. Dress,Diana Dudziak,Michael L. Dustin,Charles-Antoine Dutertre,Charles-Antoine Dutertre,Friederike Ebner,Sidonia B G Eckle,Matthias Edinger,Pascale Eede,Götz R. A. Ehrhardt,Marcus Eich,Pablo Engel,Britta Engelhardt,Anna Erdei,Charlotte Esser,Bart Everts,Maximilien Evrard,Christine S. Falk,Todd A. Fehniger,Mar Felipo-Benavent,Helen Ferry,Markus Feuerer,Andrew Filby,Kata Filkor,Simon Fillatreau,Marie Follo,Irmgard Förster,John Bellamy Foster,Gemma A. 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TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
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