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https://github.com/tian-dechao/diffdomain
DiffDomain is a statistically sound method for detecting differential TADs between conditions
https://github.com/tian-dechao/diffdomain
3d-genome differential hi-c python r tads
Last synced: 25 days ago
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DiffDomain is a statistically sound method for detecting differential TADs between conditions
- Host: GitHub
- URL: https://github.com/tian-dechao/diffdomain
- Owner: Tian-Dechao
- License: mit
- Created: 2021-11-03T04:24:33.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-22T13:59:05.000Z (3 months ago)
- Last Synced: 2024-09-10T09:56:51.441Z (about 2 months ago)
- Topics: 3d-genome, differential, hi-c, python, r, tads
- Language: Python
- Homepage:
- Size: 11.1 MB
- Stars: 13
- Watchers: 3
- Forks: 4
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# diffDomain
## A short description
diffDomain is a new computational method for identifying reorganized TADs using chromatin contact maps from two biological conditions.
## A long description diffDomain
The workflow of diffDomain is illustrated down below.
The goal is to test if a TAD identified in one biological condition has structural changes in another biological condition.
The core of diffDomain is formulating the problem as a hypothesis testing problem where the null hypothesis is that the TAD doesn't undergo significant structural reorganization at later condition.
The input are Hi-C contact matrices of the TAD region in the two biological conditions (*A*).
The Hi-C contact matrices are log-transformed to adjust for the exponential decay of Hi-C contacts between chromosome bins with increased distances.
Their entry-wise difference is calculated (*B*).
The difference matrix *D* is normalized by iteratively standardizing its *k*-off diagonal parts, *-N+2 <= k <= N-2*, adjusting absolute differences in contact frequencies due to different sequencing depths in the two biological conditions (*C*).
Note that, standardization is TAD-specific. Each TAD has its own parameters that are only estimated from its contact matrices in a pair of biological conditions.
Intuitively, if a TAD is not significantly reorganized, normalized *D* would resemble a random matrix with white noise entries, enabling us to borrow theoretical results in random matrix theory.
Indeed, normalized *D* is a generalized Wigner matrix (D), a well studied high-dimensional random matrices.
Its largest singular value is proved to be fluctuating around 2 under the null hypothesis.
Armed with the fact, diffDomain reformulates the reorganized TAD identification problem into a hypothesis testing problem:
1. H0: the largest singular value equals to 2;
2. H1: the largest singular value is greater than 2.
For a user given set of TADs, *P* values are adjusted for multiple comparisons using *BH* method as default.
Once we identify the subset of reorganized TADs, we classify them into six subtypes to aid biological analysis and interpretations (F).
A few examples of reorganized TADs identified by diffDomain in two datasets are shown in (G).
## Installation instructions
diffDomain is tested on MacOS & Linux (Centos).
## Dependences
diffDomain-py2 is dependent on
- Python 2.7
- hic-straw==0.0.6
diffDomain-py3 is dependent on
- Python 3
- hic-straw==1.3.1
and
- cooler
- hicexplorer
- TracyWidom
- pandas
- numpy
- docopt
- tqdm
- matplotlib
- statsmodels
- h5py
- seaborn
## Installation
First of all, we recommend you to have a package manager, such as [conda](https://docs.conda.io/en/latest/miniconda.html), and create a new independent environment for diffDomain.
### Method1: to install the conda environment
Step1:
```
git clone https://github.com/Tian-Dechao/diffDomain
cd diffDomain
```
Step2:
For Linux
```
conda env create --name diffdomain -f environment_linux.yml
```
For MacOS
```
conda env create --name diffdomain -f environment_macos.yml
```
Step3:
```
conda activate diffdomain
```
In this environment, all the need of diffDomain(Python3 version) have been installed.
### Method2: to install python3 version from Pypi
```
pip install diffDomain-py3
```
Note: If you encounter errors when installing hicstraw that diffDomain relies on, you can use [conda](https://docs.conda.io/en/latest/miniconda.html) to install it:
```
conda install -c bioconda hic-straw
```
### Method3: Docker image named guming5/diffdomain-centos7:v1
```
docker pull guming5/diffdomain-centos7:v1
docker run -it guming5/diffdomain-centos7:v1 /bin/bash
# shift to the normal user named work
su work
cd ~
source activate diffdomain
```
In this image, there is a contact conda environment named diffdomain (/home/work/.conda/envs/diffdomain) meeting all requests, in which you can use the diffDomain Python3 version directly.
## Documentation
Please see the [wiki](https://github.com/Tian-Dechao/diffDomain/wiki/0.Usage) for extensive documentation and example tutorials.
# Contact information
More information please contact Dunming Hua at [email protected], Ming Gu at [email protected] or Dechao Tian at [email protected].
# References
[](https://zenodo.org/doi/10.5281/zenodo.10205207)