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CLARITY: comparing heterogeneous data using dissimilarity.

Daniel J Lawson1,2, Vinesh Solanki3, Igor Yanovich4

  • 1Institute of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK.

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|December 15, 2021
PubMed
Summary
This summary is machine-generated.

Integrating diverse scientific datasets is challenging. Our new method, CLARITY, quantifies cross-dataset consistency, identifies inconsistencies, and aids interpretation across fields like genomics and social sciences.

Keywords:
comparitive statisticslinguisticsvisualization

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Area of Science:

  • Multidisciplinary data integration
  • Computational biology
  • Quantitative social sciences

Background:

  • Integrating heterogeneous datasets poses significant challenges due to variations in meaning, scale, and reliability.
  • Scientific inquiry often requires assessing the conservation of entity similarities across disparate data sources.

Purpose of the Study:

  • To introduce CLARITY, a novel method for quantifying consistency across datasets.
  • To identify and interpret inconsistencies arising from cross-dataset comparisons.
  • To provide a robust framework for analyzing relationships between different data modalities.

Main Methods:

  • CLARITY employs a non-parametric approach robust to noise and scaling differences.
  • It decomposes similarity matrices into 'structural' and 'relationship' components for comparison.
  • Significance is assessed using dataset-appropriate resampling techniques.

Main Results:

  • Demonstrated CLARITY's utility across diverse comparisons: gene methylation vs. expression, linguistic evolution, and economic vs. cultural metrics.
  • Quantified cross-dataset consistency and pinpointed areas of divergence.
  • The method proved effective in handling data with varying properties.

Conclusions:

  • CLARITY offers a powerful, flexible tool for comparing datasets from different scientific disciplines.
  • The method facilitates a deeper understanding of conserved and divergent patterns across data types.
  • Available as an R package, CLARITY promotes reproducible cross-disciplinary research.