# Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO

## TL;DR

TREASMO is a Python tool that helps analyze and visualize gene regulation in single-cell multi-omics data.

## Contribution

TREASMO introduces a novel correlation strength index for identifying regulatory changes at single-cell resolution.

## Key findings

- TREASMO accurately quantifies gene–peak correlation strength in single-cell data.
- The tool identifies regulatory markers and uncovers temporal patterns in hematopoietic cells.
- TREASMO enables comprehensive exploration of transcriptional regulatory dynamics.

## Abstract

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene–peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO’s capacity in quantifying the gene–peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.