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:

Original author: Aoyan Dong

Current maintainer: Junhao Wen

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.

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

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

pip install -e .

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

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.

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

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)

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