We developed an efficient and accurate multi-omics differential network analysis tool – integrative Differential Dependency Networks (iDDN). iDDN is capable of jointly learning sparse common and rewired network structures, which is especially useful for genomics, proteomics, and other biomedical studies. This repository provides the source code and examples of using iDDN.
iDDN can then be installed with the following command.
pip install iddnIf you meet any issue, one way is to install iDDN into a new Conda environment.
To create and activate an environment named iddn, run this:
conda create -n iddn python=3.11
conda activate iddnThen run pip install iddn.
Alternatively, you can clone the repository, or just download or unzip it. Then we can install DDN 3.0.
$ pip install ./Or you may want to install it in development mode.
$ pip install -e ./This toy example applies iDDN on a synthetic data. More details can be found in the first tutorial.
from iddn import tools # Run iDDN algorithm and process the output
from iddn_data import load_data # Load example data and images
example = load_data.load_example("example.npz")
dat1 = example["dat1"]
dat2 = example["dat2"]
dep_mat = example["dep_mat"]
result = tools.iddn_basic_pipeline(dat1, dat2, dep_mat, lambda1=0.15, lambda2=0.05)For more details and examples, check the documentation,
which includes four tutorials and the API reference.
The tutorials can also be found in the docs/notebooks folder.
For details about the code to run simulations and make figures in the paper, check out the repository here.
To run tests, go to the folder of DDN3 source code, then run pytest.
pytest testsIt will compare output of DDN with reference values. It tests iDDN with two acceleration strategies.
Please report bugs in the issues or email Yizhi Wang (yzwang@vt.edu). If you are interested in adding features or fixing bug, feel free to contact us.
The iddn package is licensed under the terms of the MIT license.
[1] Yizhi Wang, Yi Fu, et al. "iDDN: Determining trans-omics network structure and rewiring with integrative differential dependency networks".