FairSubset: A tool to choose representative subsets of data for use with replicates or groups of different sample sizes.

Autor: Ortell KK; Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA., Switonski PM; Departments of Neurology, Duke University School of Medicine, Durham, NC 27710, USA.; The Duke Center for Neurodegeneration & Neurotherapeutics, Duke University School of Medicine, Durham, NC 27710, USA., Delaney JR; Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.
Jazyk: angličtina
Zdroj: Journal of biological methods [J Biol Methods] 2019 Sep 03; Vol. 6 (3), pp. e118. Date of Electronic Publication: 2019 Sep 03 (Print Publication: 2019).
DOI: 10.14440/jbm.2019.299
Abstrakt: High-impact journals are promoting transparency of data. Modern scientific methods can be automated and produce disparate samples sizes. In many cases, it is desirable to retain identical or pre-defined sample sizes between replicates or groups. However, choosing which subset of originally acquired data that best matches the entirety of the data set without introducing bias is not trivial. Here, we released a free online tool, FairSubset, and its constituent Shiny App R code to subset data in an unbiased fashion. Subsets were set at the same N across samples and retained representative average and standard deviation information. The method can be used for quantitation of entire fields of view or other replicates without biasing the data pool toward large N samples. We showed examples of the tool's use with fluorescence data and DNA-damage related Comet tail quantitation. This FairSubset tool and the method to retain distribution information at the single-datum level may be considered for standardized use in fair publishing practices.
Competing Interests: Competing interests: The authors have declared that no competing interests exist.
(© 2013-2019 The Journal of Biological Methods, All rights reserved.)
Databáze: MEDLINE