A procedure to detect general association based on concentration of ranks

TL;DR

RankCover is a new non-parametric method that detects various types of associations between variables by measuring the concentration of ranked data points, demonstrating robustness and high power in simulations and real data.

Contribution

The paper introduces RankCover, a novel association test based on rank concentration and disk-covering statistics, enhancing detection of diverse relationships in high-throughput data.

Findings

RankCover outperforms existing methods in simulated datasets.

It is robust across different types of associations.

Effective in real-world high-throughput data analysis.

Abstract

In modern high-throughput applications, it is important to identify pairwise associations between variables, and desirable to use methods that are powerful and sensitive to a variety of association relationships. We describe RankCover, a new non-parametric association test for association between two variables that measures the concentration of paired ranked points. Here `concentration' is quantified using a disk-covering statistic that is similar to those employed in spatial data analysis. Analysis of simulated datasets demonstrates that the method is robust and often powerful in comparison to competing general association tests. We illustrate RankCover in the analysis of several real datasets.