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SCEVAN: Single-Cell Variational Aneuploidy Analysis

SCEVAN Logo

📚 Documentation: https://zaoqu-liu.github.io/SCEVAN/

Overview

SCEVAN (Single CEll Variational Aneuploidy aNalysis) is a computational framework for automated tumor cell identification and copy number alteration (CNA) inference from single-cell RNA sequencing (scRNA-seq) data. The algorithm employs a variational segmentation approach combined with a hierarchical hidden Markov model to accurately delineate clonal copy number substructures within heterogeneous tumor populations.

SCEVAN Workflow

Key Features

  • Automated Cell Classification: Distinguishes malignant cells from non-malignant tumor microenvironment (TME) cells without prior knowledge
  • Clonal Structure Inference: Identifies subclonal populations with distinct copy number architectures
  • High Performance: Achieves F1 score of 0.90 across 106 samples (93,332 cells), outperforming state-of-the-art methods (F1 = 0.63)
  • Computational Efficiency: Employs greedy multichannel segmentation for scalability to large datasets
  • Cross-Platform Compatibility: Full support for Windows, macOS, and Linux
  • Seurat Integration: Compatible with Seurat v4 and v5 workflows

Publication

De Falco, A., Caruso, F., Su, X.-D., Varone, A., & Ceccarelli, M. (2023). A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. Nature Communications, 14, 1074.

DOI

Installation 

install.packages("SCEVAN", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub 

# Install dependencies
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("EnsDb.Hsapiens.v86", "EnsDb.Mmusculus.v79", "scran", "fgsea"))

# Install yaGST dependency
remotes::install_github("miccec/yaGST")

# Install SCEVAN
remotes::install_github("Zaoqu-Liu/SCEVAN")

Quick Start 

library(SCEVAN)

# Basic analysis with default parameters
results <- pipelineCNA(count_mtx, sample = "sample_name")

Usage

Single-Sample Analysis

The pipelineCNA() function performs the complete analysis pipeline including cell classification and clonal structure characterization.

results <- pipelineCNA(
  count_mtx,                    # Raw count matrix (genes × cells)
  sample = "sample_name",       # Sample identifier
  par_cores = 20,               # Number of parallel cores
  organism = "human",           # "human" or "mouse"
  SUBCLONES = TRUE,             # Enable subclone detection
  beta_vega = 0.5,              # Segmentation granularity parameter
  ClonalCN = TRUE,              # Infer clonal CN profile
  plotTree = FALSE              # Plot phylogenetic tree
)

Parameters

Parameter Description Default
count_mtx Raw count matrix with genes on rows and cells on columns Required
sample Sample name for output files ""
par_cores Number of parallel cores 20
norm_cell Known normal cells for reference NULL
SUBCLONES Enable subclonal analysis TRUE
beta_vega Segmentation parameter (higher = coarser) 0.5
ClonalCN Infer clonal CN profile TRUE
plotTree Generate phylogenetic tree plot FALSE
organism Species ("human" or "mouse") "human"
FIXED_NORMAL_CELLS Use provided normal cells as fixed reference FALSE

Multi-Sample Comparison

For comparative analysis across multiple samples:

multiSampleComparisonClonalCN(
  listCountMtx,                 # Named list of count matrices
  listNormCells = NULL,         # List of normal cells per sample
  analysisName = "comparison",  # Analysis identifier
  organism = "human",
  par_cores = 20
)

Seurat Integration

SCEVAN integrates seamlessly with Seurat workflows:

# Run SCEVAN analysis
results <- pipelineCNA(count_mtx)

# Add to existing Seurat object
seurat_obj <- Seurat::AddMetaData(seurat_obj, metadata = results)

# Or create new Seurat object with SCEVAN annotations
seurat_obj <- Seurat::CreateSeuratObject(count_mtx, meta.data = results)

Extract Count Matrix from Seurat Object 

# Compatible with Seurat v4 and v5
count_mtx <- getCountMtxFromSeurat(seurat_obj, assay = "RNA")

Visualizing Region-Specific CNAs 

# Load analysis outputs
load("output/sample_count_mtx_annot.RData")
load("output/sample_CNAmtx.RData")

# Extract CN ratio for specific genomic region
region_cn <- apply(
  CNA_mtx_relat[count_mtx_annot$seqnames == 3 & 
                count_mtx_annot$start >= 158644278 & 
                count_mtx_annot$end <= 194498364, ], 
  2, mean
)

# Visualize with Seurat
seurat_obj <- AddMetaData(seurat_obj, region_cn, col.name = "chr3_cn")
FeaturePlot(seurat_obj, "chr3_cn", cols = c("gray", "red"))

CNA Heatmap with Annotations

Generate annotated CNA heatmaps incorporating cell metadata:

plotCNA_withAnnotCells(
  SampleName = "sample_name",
  metadata = cell_annotations,      # data.frame with cell annotations
  COLUMNS_TO_PLOT = c("cell_type", "cluster"),
  SUBCLONE = TRUE                   # Plot subclone-specific profiles
)

Annotated CNA Heatmap

Vignettes

Detailed tutorials demonstrating SCEVAN functionality:

Docker

A containerized version is available for reproducible analyses:

# Pull Docker image
docker pull anthonyphis/r_scevan

# Run analysis
docker run -v /path/to/data:/home/data -it anthonyphis/r_scevan:latest \
    Rscript /home/data/analysis_script.R

Example Datasets

Pre-processed datasets for testing and tutorials:

Dataset Description Download
MGH106 Glioblastoma (GSE131928) Download
listCountMtx MGH102/104/105/106 combined Download
HNSCC26 Head & Neck Cancer (GSE10332) Download

Citation

If you use SCEVAN in your research, please cite:

@article{DeFalco2023,
  author  = {De Falco, Antonio and Caruso, Francesca and Su, Xiao-Dong and 
             Varone, Antonio and Ceccarelli, Michele},
  title   = {A variational algorithm to detect the clonal copy number 
             substructure of tumors from scRNA-seq data},
  journal = {Nature Communications},
  year    = {2023},
  volume  = {14},
  pages   = {1074},
  doi     = {10.1038/s41467-023-36790-9}
}

License

This project is licensed under the GPL-2 License - see the LICENSE file for details.

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