H
Héctor Corrada Bravo
Researcher at University of Maryland, College Park
Publications - 96
Citations - 11136
Héctor Corrada Bravo is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Bioconductor & Epigenomics. The author has an hindex of 30, co-authored 94 publications receiving 8402 citations. Previous affiliations of Héctor Corrada Bravo include Johns Hopkins University School of Medicine & Johns Hopkins University.
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Journal ArticleDOI
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber,Vincent J. Carey,Robert Gentleman,Simon Anders,Marc R. J. Carlson,Benilton S. Carvalho,Héctor Corrada Bravo,Sean Davis,Laurent Gatto,Thomas Girke,Raphael Gottardo,Florian Hahne,Kasper D. Hansen,Rafael A. Irizarry,Michael S. Lawrence,Michael I. Love,James W. MacDonald,Valerie Obenchain,Andrzej K. Oleś,Hervé Pagès,Alejandro Reyes,Paul Shannon,Gordon K. Smyth,Dan Tenenbaum,Levi Waldron,Martin Morgan +25 more
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.
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Tackling the widespread and critical impact of batch effects in high-throughput data
Jeffrey T. Leek,Robert B. Scharpf,Héctor Corrada Bravo,Héctor Corrada Bravo,David M. Simcha,Benjamin Langmead,W. Evan Johnson,Donald Geman,Keith A. Baggerly,Rafael A. Irizarry +9 more
TL;DR: It is argued that batch effects (as well as other technical and biological artefacts) are widespread and critical to address and experimental and computational approaches for doing so are reviewed.
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Differential abundance analysis for microbial marker-gene surveys
TL;DR: It is shown that metagenomeSeq outperforms the tools currently used in this field and relies on a novel normalization technique and a statistical model that accounts for undersampling in large-scale marker-gene studies.
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Increased methylation variation in epigenetic domains across cancer types
Kasper D. Hansen,Winston Timp,Winston Timp,Héctor Corrada Bravo,Héctor Corrada Bravo,Sarven Sabunciyan,Benjamin Langmead,Benjamin Langmead,Oliver G. McDonald,Bo Wen,Hao Wu,Yun Liu,Dinh Diep,Eirikur Briem,Kun Zhang,Rafael A. Irizarry,Rafael A. Irizarry,Andrew P. Feinberg +17 more
TL;DR: Stochastic methylation variation of the same cDMRs, distinguishing cancer from normal tissue, is shown in colon, lung, breast, thyroid and Wilms' tumors, with intermediate variation in adenomas.
Journal ArticleDOI
Multivariable association discovery in population-scale meta-omics studies.
Himel Mallick,Himel Mallick,Ali Rahnavard,Lauren J. McIver,Lauren J. McIver,Siyuan Ma,Siyuan Ma,Yancong Zhang,Yancong Zhang,Long H. Nguyen,Timothy L. Tickle,George Weingart,George Weingart,Boyu Ren,Boyu Ren,Emma Schwager,Emma Schwager,Suvo Chatterjee,Kelsey N. Thompson,Jeremy E. Wilkinson,Ayshwarya Subramanian,Ayshwarya Subramanian,Yiren Lu,Levi Waldron,Joseph N. Paulson,Eric A. Franzosa,Eric A. Franzosa,Héctor Corrada Bravo,Curtis Huttenhower,Curtis Huttenhower +29 more
TL;DR: MaAsLin 2 (Microbiome Multivariable Associations with Linear Models) as mentioned in this paper uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types with or without covariates and repeated measurements.