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

Area of Science:

• Computational Biology
• Genomics
• Molecular Biology

Background:

• Single-cell multi-omics technologies offer deep insights into cellular regulatory mechanisms.
• Understanding transcriptional regulatory dynamics is crucial for deciphering complex biological processes.

Purpose of the Study:

• To introduce TREASMO, a Python package for quantifying and visualizing single-cell transcriptional regulatory dynamics.
• To enable comprehensive exploration of single-cell multi-omics datasets through integrated modules.

Main Methods:

• Development of a novel single-cell gene-peak correlation strength index.
• Implementation of four modules: data preparation, correlation quantification, downstream analysis, and visualization.
• Validation using a hematopoietic stem and progenitor cell dataset.

Main Results:

• TREASMO accurately quantifies gene-peak correlation strength at the single-cell level.
• The package facilitates the identification of key regulatory markers.
• Temporal regulatory patterns within cell trajectories can be discovered.

Conclusions:

• TREASMO provides a versatile tool for analyzing single-cell multi-omics data.
• It enhances the ability to identify and understand regulatory dynamics at single-cell resolution.
• The package aids in discovering regulatory markers and temporal patterns in biological systems.