Note

This R package is currently in development and therefore is not functional yet

muscadet

multiomics single-cell copy number alterations detection

R package for the identification of copy number alterations (CNAs) in cancer cells from single-cell multiomics data.

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

Install the latest version directly from GitHub

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

Use muscadet with example data

library(muscadet)

# Create muscomic objects
atac <- CreateMuscomicObject(
    type = "ATAC",
    mat_counts = mat_counts_atac_tumor,
    allele_counts = allele_counts_atac_tumor,
    features = peaks)
rna <- CreateMuscomicObject(
    type = "RNA",
    mat_counts = mat_counts_rna_tumor,
    allele_counts = allele_counts_rna_tumor,
    features = genes)
atac_ref <- CreateMuscomicObject(
    type = "ATAC",
    mat_counts = mat_counts_atac_ref,
    allele_counts = allele_counts_atac_ref,
    features = peaks)
rna_ref <- CreateMuscomicObject(
    type = "RNA",
    mat_counts = mat_counts_rna_ref,
    allele_counts = allele_counts_rna_ref,
    features = genes)

# Create raw muscadet objects
muscadet <- CreateMuscadetObject(
    omics = list(ATAC = atac, RNA = rna),
    bulk.lrr = bulk_lrr,
    bulk.label = "WGS",
    genome = "hg38")
muscadet_ref <- CreateMuscadetObject(
    omics = list(ATAC = atac_ref, RNA = rna_ref),
    genome = "hg38")

# Compute log R ratios from scATAC-seq read counts
muscadet <- computeLogRatio(
    x = muscadet,
    reference = muscadet_ref,
    omic = "ATAC",
    method = "ATAC",
    minReads = 1,
    minPeaks = 1)

# Compute log R ratios from scRNA-seq read counts
muscadet <- computeLogRatio(
    x = muscadet,
    reference = muscadet_ref,
    omic = "RNA",
    method = "RNA",
    refReads = 2)

# Cluster cells based on log R ratio data
muscadet <- clusterMuscadet(
    muscadet,
    dist_method = "euclidean",
    hclust_method = "ward.D",
    k_range = 2:5)

# You can also load the example muscadet object
load(muscadet_obj)

# Plot heatmap of clustering
heatmapMuscadet(
    muscadet,
    k = 3,
    show_missing = FALSE,
    filename = "heatmap_muscadet_k3_commoncells.png",
    title = "Example sample (k=3)")
heatmapMuscadet(
    muscadet,
    k = 3,
    filename = "heatmap_muscadet_k3_allcells.png",
    title = "Example sample (k=3)")

# Plot Silhouette widths for cluster validation
plotSil(muscadet, k = 3)

# Plot cluster validation indexes
plotIndexes(muscadet)
plotIndexes(muscadet, index = "silhouette")

# Assign the chosen cluster assignment
muscadet <- assignClusters(muscadet, k = 3)

# Merge all counts from all omics from both sample and reference
muscadet <- mergeCounts(muscadet, muscadet_ref)

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Detection of Somatic Copy Number Alterations from Single-Cell Multiomics Data

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.

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