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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Analysing extremely small sized ratio datasets.

Piero Ricchiuto1, Judy C G Sng2, Wilson Wen Bin Goh3

  • 11 Cardiovascular Division, Harvard Medical School, 25 Shattuck St Boston, MA 02115, USA.

International Journal of Bioinformatics Research and Applications
|November 13, 2015
PubMed
Summary

Analyzing extremely small sized ratio (ESSR) data is challenging. This study introduces Dodeca-Panels, a novel plot-based method generalizing the Bland-Altman plot for statistically meaningful analysis of limited biological data.

Keywords:
bioinformaticsbiomarker discoveryclinical translationdrug discoveryprotein respondersproteomicssmall ratio datasets

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Area of Science:

  • Bioinformatics
  • Statistical Analysis
  • Genomics

Background:

  • High-throughput biological studies often face limitations with small sample sizes, hindering statistically meaningful analysis of expression ratios.
  • Traditional methods like single sample Z-tests or network inference have limitations when dealing with extremely small sized ratio (ESSR) data.

Purpose of the Study:

  • To introduce a novel plot-based approach, Dodeca-Panels, for analyzing ESSR data.
  • To provide a complementary method to existing workarounds for small sample size issues in biological studies.

Main Methods:

  • Generalization of the Bland-Altman plot to create Dodeca-Panels.
  • Development of an R implementation for the Dodeca-Panels method.

Main Results:

  • The Dodeca-Panels method offers a visual and analytical tool for ESSR data.
  • An R package is provided for practical application of the Dodeca-Panels approach.

Conclusions:

  • Dodeca-Panels provides a valuable new method for analyzing biological data with extremely small sample sizes.
  • The R implementation facilitates the adoption of this technique in high-throughput studies.