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Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS
Remi Trimbour1,2, Ina Maria Deutschmann2, Laura Cantini1,2
1Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France.
Bioinformatics (Oxford, England)
|March 9, 2024
Summary
HuMMuS infers regulatory mechanisms from single-cell multi-omics data, improving predictions of transcription factor targets and regulatory regions. This method accurately clusters cell types and identifies key transcription factors in mouse brain data.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Cellular molecular identity arises from complex interactions across regulatory layers.
- Single-cell sequencing technologies enable the measurement of these regulatory layers.
Purpose of the Study:
- To introduce HuMMuS, a novel computational method for inferring regulatory mechanisms from single-cell multi-omics data.
- To enhance the analysis of regulatory networks by capturing macromolecular cooperation and integrating multiple molecular layers.
Main Methods:
- HuMMuS was developed to analyze single-cell multi-omics datasets.
- The method was benchmarked against state-of-the-art approaches using both paired and unpaired multi-omics data.
- HuMMuS was applied to single-nucleus methylome sequencing (snmC-seq), single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), and single-cell RNA sequencing (scRNA-seq) data from mouse brain cortex.
Main Results:
- HuMMuS demonstrated improved prediction accuracy for transcription factor (TF) targets, TF binding motifs, and regulatory regions compared to existing methods.
- Application to mouse brain multi-omics data resulted in accurate clustering of single-cell RNA sequencing profiles.
- The method successfully identified potential driver TFs regulating cellular states.
Conclusions:
- HuMMuS offers a powerful approach for dissecting regulatory mechanisms from single-cell multi-omics data.
- The method's ability to capture macromolecular cooperation and integrate diverse regulatory layers advances the field of systems biology.
- HuMMuS provides valuable insights into cell type classification and the identification of key regulatory factors in complex biological systems.
