Read-level methylome simulator using a beta-binomial distribution.
It currently supports only two cell-type simulations (tumour and normal).
Pseudo-bulk samples with random cell-type compositions can be also simulated with the read-level methylomes.
methylseq_simulation requires Python version > 3.6 (So far, it has been tested under Python version 3.7, 3.8 and 3.9).
The dependencies are clarified in requirements.txt.
If your environment (e.g. conda, Python venv and so on) is already activated, you can simply install the dependencies by pip install -r requirements.txt
Conda is a package and environment manager. It helps you with managing software dependencies independently from your local system. If you want to start conda, please find their installation guidance here.
- Create a new conda environment with a specific python version
conda creat -n $your_env_name python=$python_version - Install the dependencies by
pip install -r requirements.txt
Python also supports a virtual environment.
- Create a Python virtual environment by
python -m venv $your_directory_path - Activate the virtual environment by
source $your_directory_path/bin/activate - Install dependencies by
pip install -r requirements.txt
Docker also provides resources to manage environment called container. We provide Dockerfile to run the simulator with the required dependencies.
- Build a docker container by
sudo docker build -t methylseq_simulation . - Run the container by
sudo docker run -i -t methylseq_simulation /bin/bash - You can use the command line as described in Quick start
If you get a version-related error message as below:
Could not find a version that satisfies the requirement numpy==1.21.4
(from versions: 1.14.5, 1.14.6, 1.15.0, 1.15.1, 1.15.2, 1.15.3, 1.15.4,
1.16.0, 1.16.1, 1.16.2, 1.16.3, 1.16.4, 1.16.5, 1.16.6, 1.17.0, 1.17.1,
1.17.2, 1.17.3, 1.17.4, 1.17.5, 1.18.0, 1.18.1, 1.18.2, 1.18.3, 1.18.4,
1.18.5, 1.19.0, 1.19.1, 1.19.2, 1.19.3, 1.19.4, 1.19.5, 1.20.0, 1.20.1,
1.20.2, 1.20.3, 1.21.0, 1.21.1, 1.21.2, 1.21.3)
You may want to upgrade your pip by pip install --upgrade pip
The simulator requires a reference genome FASTA file (such as hg19.fa) to simulate the reads. You can download various genome sequences from UCSC as a gzipped FASTA file.
If you have a specific set of regions you want to simulate reads, you can give the file name with -d option.
The file must be tab-separated and include chromosome, start and end information with chr, start and end for column names.
You can simulate read-level methylomes and pseudo-bulk samples (with --bulk option) by running main.py as below:
python main.py --help
usage: main.py [-h] -r F_REF [-d F_REGION] [-o OUTPUT_DIR] [--save_img]
[-ng N_REGIONS] [-a A] [-b B] [-nr N_READS] [-k K_MER]
[-l LEN_READ] [--seed SEED] [--bulk] [-nb N_BULKS] [-s STD]
optional arguments:
-h, --help show this help message and exit
-r F_REF, --f_ref F_REF
.fasta file for the reference genome
-d F_REGION, --f_region F_REGION
tab-separated .csv file, DMRs should be given with
mean methylation level of each cell type, chr, start
and end. n_regions will be selected from hg19 CpG
islands if the file is not given
-o OUTPUT_DIR, --output_dir OUTPUT_DIR
a directory where all generated results will be saved
(default: ./)
--save_img Save simulated methylation patterns as a .png file
(default: False)
-ng N_REGIONS, --n_regions N_REGIONS
Number of regions to select from CGIs when the region
file is not given (default: 100)
-a A, --a A alpha parameter of the beta-binomial distribution to
simulate read-level methylomes (default: 1e-6)
-b B, --b B beta parameter of the beta-binomial distribution to
simulate read-level methylomes (default: 5)
-nr N_READS, --n_reads N_READS
Read coverage to simulate in each DMR (default: 120)
-k K_MER, --k_mer K_MER
K to process the simulated read sequences into K-mer
sequence (default: 1)
-l LEN_READ, --len_read LEN_READ
Read length to simulate (default: 150)
--seed SEED seed number (default: 950410)
--bulk Whether you want to generate pseudo-bulks or the
entire dataset (default: False)
-nb N_BULKS, --n_bulks N_BULKS
Number of bulks, Applicable only when --bulk is True
(default: 1)
-s STD, --std STD Standard deviation to sample local proportions. The
larger value is given, the more varying local
proportions are sampled from a Gaussian distribution
centred at the global proportion (default: 0.0)
If --save_img option is on, you can get the summary of results as figures in the designated output_dir.
