Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

E. Sweeney, Timothy; D. Azad, Tej; Donato, Michele; A. Haynes, Winston; M. Perumal, Thanneer; Henao, Ricardo; F. Bermejo-Martin, Jesús; Almansa, Raquel; Tamayo, Eduardo; A. Howrylak, Judith; Choi, Augustine; P. Parnell, Grant; Tang, Benjamin; Nichols, Marshall; W. Woods, Christopher; S. Ginsburg, Geoffrey; F. Kingsmore, Stephen; Omberg, Larsson; M. Mangravite, Lara; R. Wong, Hector; L. Tsalik, Ephraim; J. Langley, Raymond; Khatri, Purvesh

doi:10.1097/ccm.0000000000003084

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Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

Shared by NobleBlocks on Jun 1, 2018 • 12:00 AM UTC

Authors:

Timothy E. Sweeney

Tej D. Azad

Michele Donato

Abstract

Objectives: To find and validate generalizable sepsis subtypes using data-driven clustering. Design: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries ( n = 700). Setting: Retrospective analysis. Subjects: Persons adm...

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