Multiomics Single-Cell Copy Number Alterations Detection

muscadet is an R package for identifying copy number alterations (CNAs) in cancer cells from single-cell multiomics data.

Key features of `muscadet`:

Data: Copy number analysis from a single omic to multiple omics at once on the same cells.
Integration: Integrate information at low-level from the matched omics.
Clustering: Cluster cells based on genome-wide coverage profiles.
Imputation: Impute cluster for cells missing data in one of the omic by nearest neighbor similarity.
Detection: Detect and call CNA segments using both coverage (log ratio of read counts) and allelic imbalance (read counts per allele).
Visualization: Explore genome-wide coverage, clusters of cells, UMAP embeddings, CNA profiles, and more.

Installation

You can install the latest version of muscadet from GitHub:

library(devtools)
devtools::install_github("ICAGEN/muscadet")

To get started, read the Get Started vignette to learn about the muscadet workflow and function usage on demo example data.
To prepare inputs, read the Preparation of input data vignette to get details on the inputs for muscadet and how to prepare them.

Citation

To cite the muscadet package in publications, use:

Denoulet M, Giordano N, Minvielle S, Vallot C, Letouzé E (2025). muscadet: Multiomics Single-Cell Copy Number Alterations Detection. R package version 0.2.0, https://icagen.github.io/muscadet/, https://github.com/ICAGEN/muscadet.

Communications

[Poster] Detection of Somatic Copy Number Alterations from Single-cell Multiomics Data with the R Package muscadet Marie Denoulet, Nils Giordano, Mia Cherkaoui, Elise Douillard, Magali Devic, Florence Magrangeas, Stéphane Minvielle, Céline Vallot, Eric Letouzé. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM 2025), Jul 2025, Bordeaux, France. ⟨hal-05150254⟩
[Poster] Detection of Somatic Copy Number Alterations from Single-cell Multiomics Data Marie Denoulet, Nils Giordano, Mia Cherkaoui, Elise Douillard, Magali Devic, Florence Magrangeas, Stéphane Minvielle, Céline Vallot, Eric Letouzé. 23rd European Conference on Computational Biology (ECCB2024), Sep 2024, Turku, Finland. ⟨hal-05150028⟩

Detection of Somatic Copy Number Alterations from Single-Cell Multiomics Data with the R Package `muscadet`

Marie Denoulet¹, Mia Cherkaoui¹, Nils Giordano¹, Robin Lanée¹, Elise Douillard^1,2, Magali Devic^1,2, Florence Magrangeas^1,2, Stéphane Minvielle^1,2, Céline Vallot^3,4, Eric Letouzé^1,2

¹Nantes Université, INSERM, CNRS, Université d’Angers, CRCI2NA, Nantes, France. ²University Hospital Hôtel-Dieu, Nantes, France. ³CNRS UMR3244, Institut Curie, PSL University, Paris, France. ⁴Translational 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. The muscadet framework enables integration of joint multiomics data, such as single-cell transcriptomics (scRNA-seq) and chromatin accessibility (scATAC-seq), captured from the same cells. Cells are initially clustered by relative coverage (log ratio of read counts) integrated across multiple omics layers. CNA calling is then performed on aggregated coverage and allelic (read counts per allele) data at the cluster level, leveraging combined signals from omics and cells. Lastly, muscadet includes visualization tools for genome-wide coverage assessment, subclone identification, and comprehensive CNA profiling.

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.

muscadet