Getting Started with SCEVAN

Introduction

SCEVAN (Single CEll Variational Aneuploidy aNalysis) is a comprehensive R package for analyzing copy number alterations (CNAs) in single-cell RNA sequencing (scRNA-seq) data. This vignette provides a quick introduction to get you started with SCEVAN.

Key Capabilities

Automated Cell Classification: Distinguishes malignant cells from tumor microenvironment (TME) cells
CNA Inference: Infers copy number profiles from gene expression data
Subclone Detection: Identifies clonal subpopulations with distinct copy number architectures
Cross-Platform: Works on Windows, macOS, and Linux

Installation

From R-universe (Recommended)

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

From GitHub

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

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

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

Quick Start

Load the Package

library(SCEVAN)

Prepare Your Data

SCEVAN requires a raw count matrix with:

Rows: Gene names (Gene Symbols or Ensembl IDs)
Columns: Cell barcodes

# Example: Loading count matrix from a file
count_mtx <- read.csv("your_counts.csv", row.names = 1)

# Or from a Seurat object (v4/v5 compatible)
count_mtx <- getCountMtxFromSeurat(seurat_obj, assay = "RNA")

Run the Analysis

The main function pipelineCNA() performs the complete analysis:

results <- pipelineCNA(
  count_mtx,
  sample = "MySample",
  par_cores = 4,
  organism = "human"
)

Understand the Output

The function returns a data frame with cell classifications:

Column	Description
`class`	Cell type: “tumor”, “normal”, or “filtered”
`confidentNormal`	Whether cell was used as normal reference
`subclone`	Subclone assignment (if detected)

Output files are saved to the ./output/ directory:

*heatmap.png - CNA heatmap with cell classifications
*_CN.seg - Segmentation results
*_CNAmtx.RData - CNA matrix for downstream analysis

Next Steps

Algorithm Details: Understand the methodology behind SCEVAN
Single-Sample Analysis: Detailed walkthrough with real data
Multi-Sample Comparison: Compare CNAs across samples
Seurat Integration: Integrate with Seurat workflows

Citation

If you use SCEVAN in your research, please cite:

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. https://doi.org/10.1038/s41467-023-36790-9

Session Info

sessionInfo()

Zaoqu Liu

2026-01-24