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plethy: management of whole body plethysmography data in R.

Identifieur interne : 002A67 ( Ncbi/Curation ); précédent : 002A66; suivant : 002A68

plethy: management of whole body plethysmography data in R.

Auteurs : Daniel Bottomly [États-Unis] ; Beth Wilmot [États-Unis] ; Shannon K. Mcweeney [États-Unis]

Source :

RBID : pubmed:25924931

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English descriptors

Abstract

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.

DOI: 10.1186/s12859-015-0547-7
PubMed: 25924931

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pubmed:25924931

Le document en format XML

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<term>Genomics</term>
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<div type="abstract" xml:lang="en">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.</div>
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