Note
This R package is currently in development and therefore is not functional yet
To install the latest version directly from GitHub:
library(devtools)
install_github("ICAGEN/muscadet")
R package for the identification of copy number alterations (CNAs) in cancer cells from single-cell multiomics data.
Marie Denoulet1,2,3, Mia Cherkaoui1, Nils Giordano1, Robin Lanée1, Elise Douillard1,4, Magali Devic1,4, Florence Magrangeas1,4, Stéphane Minvielle1,4, Céline Vallot5,6, Eric Letouzé1,4
1Nantes Université, INSERM, CNRS, Université d'Angers, CRCI2NA, Nantes, France. 2SIRIC ILIAD, Angers, Nantes, France. 3SIRIC Curie, Institut Curie, Paris, France. 4University Hospital Hôtel-Dieu, Nantes, France. 5CNRS UMR3244, Institut Curie, PSL University, Paris, France. 6Translational Research Department, Institut Curie, PSL University, Paris, France
The identification of somatic copy number alterations (CNAs) in cancer cells is crucial for understanding tumor evolution, including clonal dynamics causing relapse, and identifying potential therapeutic targets. While existing tools provide valuable insights into subclonal CNAs, they are typically limited to analyzing one type of omics data. In response to the growing use of cutting-edge technologies enabling simultaneous sequencing of multiple omics from individual cells, there emerges a need for new approaches that leverage multiomics data integration to improve the detection of CNAs.
Addressing this need, we developed an R package, muscadet, that integrates multiple single-cell datasets across different omics modalities to enhance the accuracy and resolution of CNA detection within tumoral subclones. We demonstrated the potency of our approach through the analysis of single-cell Multiome data, integrating both single-cell RNA-seq and single-cell ATAC-seq datasets from a common pool of cells, across several multiple myeloma samples. muscadet outperformed existing copy number analysis tools for both scRNA-seq and scATAC-seq data, revealing accurate CNA profiles and subclones, validated by matched whole genome sequencing data.
By providing a unified CNA analysis framework applicable to any combination of single-cell omics data, muscadet empowers researchers to unravel the clonal structure of tumor samples and uncover complex genomic alterations driving cancer progression.