CLARITY -- Comparing heterogeneous data using dissimiLARITY

TL;DR

CLARITY is a non-parametric method that compares heterogeneous datasets by decomposing similarities into structural and relational components, aiding in identifying and interpreting inconsistencies across diverse scientific data.

Contribution

The paper introduces CLARITY, a novel non-parametric approach for comparing heterogeneous datasets by decomposing similarities into structural and relationship components.

Findings

Robust to noise and scaling differences

Applicable across diverse disciplines

Provides interpretable measures of dataset consistency

Abstract

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale, and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise, and aids in their interpretation. We illustrate this using three diverse comparisons: gene methylation vs expression, evolution of language sounds vs word use, and country-level economic metrics vs cultural beliefs. The non-parametric approach is robust to noise and differences in scaling, and makes only weak assumptions about how the data were generated. It operates by decomposing similarities into two components: a `structural' component analogous to a clustering, and an underlying `relationship' between those structures. This allows a `structural comparison' between two similarity matrices using their predictability from `structure'. Significance is assessed with the help of re-sampling appropriate for each dataset. The software, CLARITY, is available as an R package from https://github.com/danjlawson/CLARITY.