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

Authors

  • Katherine K Ortell Medical University of South Carolina
  • Pawel M Switonski Duke University
  • Joe R Delaney Medical University of South Carolina

DOI:

https://doi.org/10.14440/jbm.2019.299

Keywords:

statistics, normalization, automation, microscopy

Abstract

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.

Author Biography

Joe R Delaney, Medical University of South Carolina

Assistant Professor Department of Biochemistry and Molecular Biology Medical University of South Carolina, Charleston, SC 29425, USA

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Published

2019-09-03

How to Cite

1.
Ortell KK, Switonski PM, Delaney JR. FairSubset: A tool to choose representative subsets of data for use with replicates or groups of different sample sizes. J Biol Methods [Internet]. 2019Sep.3 [cited 2022May27];6(3):e118. Available from: https://jbmethods.org/jbm/article/view/299

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