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Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz.
Wagner, Justin; Kancherla, Jayaram; Braccia, Domenick; Matsumara, James; Felix, Victor; Crabtree, Jonathan; Mahurkar, Anup; Corrada Bravo, Héctor.
Affiliation
  • Wagner J; Department of Computer Science, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Kancherla J; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Braccia D; Institute for Advanced Computer Studies, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Matsumara J; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Felix V; Institute for Advanced Computer Studies, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Crabtree J; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Mahurkar A; Institute for Advanced Computer Studies, University of Maryland, College Park, College Park, Maryland, 20742, USA.
  • Corrada Bravo H; Institute for Genome Sciences, University of Maryland, Baltimore, Baltimore, Maryland, 21201, USA.
F1000Res ; 9: 601, 2020.
Article in En | MEDLINE | ID: mdl-32742640
The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual's health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Microbiota / Data Analysis Limits: Humans Language: En Journal: F1000Res Year: 2020 Document type: Article Affiliation country: United States Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Microbiota / Data Analysis Limits: Humans Language: En Journal: F1000Res Year: 2020 Document type: Article Affiliation country: United States Country of publication: United kingdom