plethy: Management of whole body plethysmography data in R

Daniel Bottomly, Beth Wilmot, Shannon K. McWeeney

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: Characterization of respiratory phenotypes can enhance complex trait and genomic studies involving allergic/autoimmune and infectious diseases. Many aspects of respiration can be measured using devices known as plethysmographs that can measure thoracic movement. One such approach (the Buxco platform) performs unrestrained whole body plethysmography on mice which infers thoracic movements from pressure differences from the act of inhalation and exhalation. While proprietary software is available to perform basic statistical analysis as part of machine's bundled software, it is desirable to be able to incorporate these analyses into high-throughput pipelines and integrate them with other data types, as well as leverage the wealth of analytic and visualization approaches provided by the R statistical computing environment. Results: This manuscript describes the plethy package which is an R/Bioconductor framework for pre-processing and analysis of plethysmography data with emphasis on larger scale longitudinal experiments. The plethy package was designed to facilitate quality control and exploratory data analysis. We provide a demonstration of the features of plethy using a dataset assessing the respiratory effects over time of SARS and Influenza infection in mice. Conclusion: The plethy package provides functionality for users to import, perform quality assessment and exploratory data analysis in a manner that allows interoperability with existing modelling tools. Our package is implemented in R and is freely available as part of the Bioconductor project

Original languageEnglish (US)
Article number134
JournalBMC bioinformatics
Issue number1
StatePublished - Dec 12 2015


  • Mouse
  • Plethysmography
  • R
  • Respiration

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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