Hi-C Pearson matrix's Estimated PC1-pattern (HiCPEP)

HiCPEP is a Python package for creating the Estimated PC1-pattern of the Hi-C Pearson matrix, which can be used for identifying the A/B compartments.

Requirements and Installation

All the programs were tested in Ubuntu 22.04.4 LTS, HiCPEP requires python3, pip and libcurl4-openssl-dev installed on your system.

For example (Paste these commands in Bash or Zsh):

sudo apt-get update
sudo apt-get install -y libcurl4-openssl-dev
sudo apt-get install -y python3
sudo apt-get install -y pip
sudo apt-get install -y git 
git clone git@github.com:ZhiRongDev/HiCPEP.git
cd HiCPEP
python3 -m pip install -e .

If you have already installed the requirements, just paste these commands:

git clone git@github.com:ZhiRongDev/HiCPEP.git
cd HiCPEP
python3 -m pip install -e .

Quick start

from hicpep import peptools

pearson_np = peptools.straw_to_pearson(
    hic_path="https://hicfiles.s3.amazonaws.com/hiseq/gm12878/in-situ/combined.hic", # Path to the Juicer's `.hic` file.
    chrom="1", 
    resolution=1000000,
    normalization="KR",
)

est_np = peptools.create_est(pearson_np=pearson_np)
print(f"est_np: {est_np}")

For more details, please check the examples. If you are interested in the programs we used for the paper, please check the code_for_paper.

References

Zhi-Rong Cheng, Jia-Ming Chang. The exploration and optimization of the chromatin compartment analysis.