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Compositional data in neuroscience: If you've got it, log it!

Paul F Smith1, Ross M Renner2, Stephen J Haslett3

  • 1Dept. of Pharmacology and Toxicology, School of Medical Sciences, Dunedin, New Zealand; The Brain Health Research Centre, University of Otago, Dunedin, New Zealand; The Brain Research New Zealand Centre of Research Excellence, Wellington, New Zealand.

Journal of Neuroscience Methods
|July 25, 2016
PubMed
Summary

Standard statistical methods are inappropriate for analyzing compositional data in neuroscience. Log ratio methods, successfully used in other fields, offer a more suitable approach for analyzing these constrained datasets.

Keywords:
Additive log ratioCentred log ratioCompositional dataIsometric log ratioLogit transformationStatistical dependence

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Area of Science:

  • Neuroscience
  • Statistics
  • Data Analysis

Background:

  • Compositional data, which sum to a constant (e.g., 100%), are prevalent in neuroscience research.
  • Examples include behavioral response time percentages and brain tissue neurochemical concentrations.
  • The unique structure of compositional data complicates standard statistical analyses, rendering methods like general linear models and principal component analysis inappropriate due to inherent correlations and data boundaries.

Purpose of the Study:

  • To highlight the limitations of standard statistical methods when applied to compositional data in neuroscience.
  • To introduce alternative, appropriate analytical techniques for compositional data analysis in neuroscience.

Main Methods:

  • Review of the challenges posed by compositional data in neuroscience.
  • Exploration of log ratio methods, specifically the isometric log ratio (ilr) transformation, as applied in geology and zoology.
  • Comparison of the suitability of classical statistical methods versus log ratio methods for compositional data.

Main Results:

  • Classical statistical methods (t-tests, ANOVAs, multivariate analyses) are fundamentally invalid for compositional data.
  • Log ratio methods have a proven track record of successful application in other scientific disciplines dealing with compositional data.
  • The isometric log ratio (ilr) method presents specific advantages for analyzing such data.

Conclusions:

  • Compositional data analysis methods, developed by statisticians and successfully employed in geology and zoology, should be adopted for neuroscience research.
  • Implementing these advanced methods will ensure more accurate and valid statistical inferences from neuroscience compositional data.