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OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data

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Pubblicato in:PLoS One vol. 15, no. 12 (Dec 2020), p. e0242933
Autore principale: Haselimashhadi, Hamed
Altri autori: Mason, Jeremy C, Mallon, Ann-Marie, Smedley, Damian, Meehan, Terrence F, Parkinson, Helen
Pubblicazione:
Public Library of Science
Soggetti:
European Molecular Biology Laboratory
United Kingdom > UK
Statistics
Software packages
Data analysis
Consortia
Reproducibility
Bioinformatics
Molecular biology
Software
Genomes
Computer applications
Statistical analysis
Statistical methods
Time series
Genotype & phenotype
Robustness
Statistical analysis of data
Computer programs
Phenotyping
Computing time
Laboratories
Environmental
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Abstract:Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.
ISSN:1932-6203
DOI:10.1371/journal.pone.0242933
Fonte:Health & Medical Collection