CHIMERA

Since the original repository of CHIMERA is not maintained anymore, the current implementation cleaned the code and upgraded it to python 3.

CHIMERA is a generative model and its core idea is shown below:

Contributors

Original author: Aoyan Dong

Current maintainer: Junhao Wen

Installation

Ananconda allows you to install, run and update python package and their dependencies. We highly recommend the users to install Anancond3 on your machine. After installing Anaconda3, there are three choices to use CHIMERA.

Use CHIMERA as a python package (TODO)

We recommend the users to use Conda virtual environment: 1) conda create --name CHIMERA python=3.7 Activate the virtual environment: 2) source activate CHIMERA Install other python package dependencies (go to the root folder of CHIMERA): 3) ./install_requirements.sh Finally, we need install CHIMERA from PyPi: 3) pip install chimera==0.0.1

Use CHIMERA from commandline (TODO):

After installing all dependencies in the requirements.txt file, go to the root folder of CHIMERA where the setup.py locates: pip install -e .

Use CHIMERA as a developer version (Recommended for now):

python -m pip install git+https://github.com/aoyandong/CHIMERA.git

Input structure

CHIMERA requires a specific input structure inspired by BIDS. Some conventions for the group label/diagnosis: -1 represents healthy control (CN) and 1 represents patient (PT); categorical variables, such as sex, should be encoded to numbers: Female for 0 and Male for 1, for instance.

Input data format

CHIMERA clusters with ROI features in feature_tsv and covariate_tsv offers covariate effects, such as age, sex or site (Note that CHIMERA treats site and other covaiates slightly different, i.e., with different distance weights). Example for feature_tsv: participant_id session_id diagnosis ROI1 ROI2 ... sub-CLNC0001 ses-M00 -1 432.1 596.9 sub-CLNC0002 ses-M00 1 398.2 601.3 sub-CLNC0003 ses-M00 -1 412.0 567.3 sub-CLNC0004 ses-M00 -1 487.4 600.1 sub-CLNC0005 ses-M00 1 346.5 529.5 sub-CLNC0006 ses-M00 1 443.2 663.2 sub-CLNC0007 ses-M00 -1 450.2 599.3 sub-CLNC0008 ses-M00 1 443.2 509.4 Example for covariate_tsv: participant_id session_id diagnosis age sex site... sub-CLNC0001 ses-M00 -1 56.1 0 1 sub-CLNC0002 ses-M00 1 57.2 0 1 sub-CLNC0003 ses-M00 -1 43.0 1 2 sub-CLNC0004 ses-M00 -1 25.4 1 2 sub-CLNC0005 ses-M00 1 74.5 1 2 sub-CLNC0006 ses-M00 1 44.2 0 3 sub-CLNC0007 ses-M00 -1 40.2 0 4 sub-CLNC0008 ses-M00 1 43.2 1 4

Example

We offer a toy dataset in the folder of CHIMERA/data.

from chimera.chimera_clustering import clustering feature_tsv="CHIMERA/data/test_feature.tsv" output_dir = "PATH_OUTPUT_DIR" k =2 covariate_tsv="CHIMERA/data/test_covariate.tsv" clustering(feature_tsv, output_dir, k, covariate_tsv)

Citing this work

If you use this software for clustering:

Dong, A., Honnorat, N., Gaonkar, B. and Davatzikos, C., 2015. CHIMERA: clustering of heterogeneous disease effects via distribution matching of imaging patterns. IEEE transactions on medical imaging, 35(2), pp.612-621. doi:10.1109/TMI.2015.2487423