scsim: Simulator of SNVs and CNAs in Single-Cell Data
scsim is a C++ simulator for generating single-cell sequencing data with both single nucleotide variants (SNVs) and copy number alterations (CNAs), including both copy gains and copy losses.
It is designed for testing and benchmarking phylogenetic and clonal inference methods under realistic single-cell mutation processes.
scsim was originally developed during the development of ScisTree (https://github.com/yufengwudcs/ScisTree). If you use scsim, please cite:
Accurate and Efficient Cell Lineage Tree Inference from Noisy Single Cell Data: the Maximum Likelihood Perfect Phylogeny Approach, Yufeng Wu, Bioinformatics, https://doi.org/10.1093/bioinformatics/btz676, 36(3):742-750, 2020.
🔍 Overview
The simulator models the joint evolution of SNVs and CNAs along a given lineage tree.
Starting from a diploid wild-type ancestor, mutations accumulate along each branch according to user-specified rates.
Simulation process
-
Initialization:
The root genotype is initialized as(2, 0)(diploid homozygous wild-type). -
Evolution along the tree:
- Point Mutations (PMs): Occur according to a Poisson process, with a user-specified mutation rate.
Each site can mutate only once, following the Infinite Sites (IS) assumption. - Copy Number Mutations (CNMs): Randomly select an allele and either duplicate or delete it with equal probability.
Multiple CNMs can occur independently along the same branch.
- Point Mutations (PMs): Occur according to a Poisson process, with a user-specified mutation rate.
-
Read simulation:
- For each extant (leaf) cell, allelic dropout is simulated by discarding alleles with a fixed dropout rate.
- If an allele is not dropped out, reads are generated with a small chance of sequencing error.
- Coverage and read-depth variance are adjustable parameters.
⚙️ Installation
Compile using any standard C++ compiler such as g++:
g++ src/scsim.cpp -O3 -o scsim
A compiled binary scsim is also available for Linux users.
🚀 Usage
Run the simulator with the following command:
./scsim tree-file num-sites num-variants-per-site error-rate dropout-rate doublet \
cnv-rate-inc cnv-rate-dec rec-rate binary droput-cell-var ave-read-depth \
std-read-depth rnd-seed fbetabinomial
| Parameter | Description |
|---|---|
tree-file |
Input tree file in Newick format with branch lengths |
num-sites |
Number of somatic single nucleotide variants (SNVs) |
num-variants-per-site |
Number of variants per site |
error-rate |
Genotype error rate |
dropout-rate |
Genotype dropout rate |
doublet |
Number of doublet errors (the total number of output genotypes will decrease by this amount) |
cnv-rate-inc |
Rate of copy number increase events |
cnv-rate-dec |
Rate of copy number decrease events |
rec-rate |
Rate of recurrent mutations |
binary |
Output mode: 0 for binary, 1 for ternary (optional) |
droput-cell-var |
Dropout cell variation (optional) |
ave-read-depth |
Average read depth (optional) |
std-read-depth |
Standard deviation of read depth (optional) |
rnd-seed |
Random seed (integer; optional) |
fbetabinomial |
Use beta-binomial for read counts if set to 1; 0 uses normal distribution (optional) |
🧩 Example
Below is an example command demonstrating a typical simulation run:
./scsim tree-example.nwk 5 1 0.01 0.4 0 0.5 0.2 0 0 0 10 4 101013 0
Example Description: This example simulates a dataset with: 10 cells, 5 somatic SNV sites (1 variant per site). Genotyping error rate of 1% and dropout rate of 40%. Copy number increase and decrease rates of 0.5 and 0.2, respectively. Mean sequencing depth of 10 reads per locus with 4 as standard deviation. Deterministic seed (101013) for reproducibility. The output will contain simulated single-cell genotypes and read counts consistent with the provided tree and mutation parameters.
Python Interface
We also provide a Python wrapper for the simulator. You can install the scsim package directly from PyPI.
Note that a working C++ compiler (g++) is required. If your system does not have one, we recommend installing it via Conda:
conda install -c conda-forge cxx-compiler
Then, install scsim in your conda env:
pip install scsim
Usage in Python
import scsim
tree = scsim.get_random_binary_tree(10) # generate a tree with 10 leaves.
data = scsim.simulate(tree, n_site=100) # simulate reads for 100 sites with default parameters.
Release files for scsim 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scsim-1.0.0.tar.gz | 75.2 kB | Details |
Built distributions (wheels)
Total release size: 13.1 MB
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