A Measurement of In-Betweenness and Inference Based on Shape Theories

TL;DR

This paper introduces a statistical framework with in-betweenness indices to determine if a subpopulation lies between two others in multivariate space, with applications in biology and neuroscience.

Contribution

It develops novel in-betweenness metrics and inference methods for multivariate subpopulation analysis, addressing a biological question about cell types.

Findings

Successfully applied to Iris data set, demonstrating method validity.

Analyzed breast cancer subtypes to assess risk differences.

Investigated neuronal cell types with electrophysiological features.

Abstract

We propose a statistical framework to investigate whether a given subpopulation lies between two other subpopulations in a multivariate feature space. This methodology is motivated by a biological question from a collaborator: Is a newly discovered cell type between two known types in several given features? We propose two in-betweenness indices (IBI) to quantify the in-betweenness exhibited by a random triangle formed by the summary statistics of the three subpopulations. Statistical inference methods are provided for triangle shape and IBI metrics. The application of our methods is demonstrated in three examples: the classic Iris data set, a study of risk of relapse across three breast cancer subtypes, and the motivating neuronal cell data with measured electrophysiological features.