# ESPClust: unsupervised identification of modifiers for the effect size profile in omics association studies

## TL;DR

ESPClust is a new method that identifies how different factors modify the strength of associations in omics studies, helping uncover hidden patterns in complex biological data.

## Contribution

ESPClust introduces an unsupervised approach to detect effect size modifiers across multiple exposures in omics data.

## Key findings

- ESPClust successfully identifies effect size modifiers in synthetic and real-world datasets.
- The method outperforms traditional analyses by uncovering nuanced modifications overlooked otherwise.
- It enables robust analysis of complex omics data even with limited sample sizes.

## Abstract

High-throughput omics technologies have revolutionized the identification of associations between individual traits and underlying biological characteristics, but still use ‘one effect-size fits all’ approaches. While covariates are often used, their potential as effect modifiers often remains unexplored.

We propose ESPClust, a novel unsupervised method designed to identify covariates that modify the effect size of associations between sets of omics variables and outcomes. By extending the concept of moderators to encompass multiple exposures, ESPClust analyses the effect size profile (ESP) to identify regions in covariate space with different ESP, enabling the discovery of subpopulations with distinct associations. Applying ESPClust to synthetic data, insulin resistance and COVID-19 symptom manifestation, we demonstrate its versatility and ability to uncover nuanced effect size modifications that traditional analyses may overlook. By integrating information from multiple exposures, ESPClust identifies effect size modifiers in datasets that are too small for traditional univariate stratified analyses. This method provides a robust framework for understanding complex omics data and holds promise for personalised medicine.

The source code ESPClust is available at https://github.com/fjpreche/ESPClust.git. It can be installed via Python package repositories as ‘pip install ESPClust==1.1.0’.