SOPNMF

SOPNMF is the fast python implementation of stochastic orthogonally projective non-negative matrix factorization

SOPNMF documentation

SOPNMF is the python implementation of the Matlab version of Orthogonal Projective Non-negative Matrix Factorization: brainparts, and its stochastic extension.

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. We also assume that the users should be familiar with command-line operations with the Linux system. There exist three ways to use the current software.

Install SOPNMF as a python package

Please follow the instructions to install SOPNMF as a python package:

i) conda create --name sopNMF python=3.6

Activate the virtual environment:

ii) source activate sopNMF

Install other python package dependencies (go to the root folder of sopNMF; I only tested the package versions listed in the bash file.):

iii) ./install_requirements.sh

Finally, we need install sopNMF from PyPi:

iv) pip install sopnmf==0.0.3

Use sopNMF from command-line in a terminal

First, you need to go the root directory of your local repository, and then run:

pip install -e .

This will allow you to run the software as command-line in the terminal. See an example here:

Use SOPNMF as a developer version

Advanced users can git clone the package locally and work from the source code:

python -m pip install git+https://github.com/anbai106/SOPNMF.git

Input structure

sopNMF requires the input (participant_tsv) to be a specific structure inspired by BIDS. The 3 columns in the tsv are participant_id, session_id and path, respectively.

Example for participant tsv:

participant_id    session_id    path
sub-CLNC0001      ses-M00      absolute_path    
sub-CLNC0002      ses-M00      absolute_path
sub-CLNC0003      ses-M00      absolute_path
sub-CLNC0004      ses-M00      absolute_path
sub-CLNC0005      ses-M00      absolute_path
sub-CLNC0006      ses-M00      absolute_path
sub-CLNC0007      ses-M00      absolute_path
sub-CLNC0008      ses-M00      absolute_path

Examples to run SOPNMF

First, if you have a population with a small to medium sample size, you can try to run the OPNMF model:

from sopnmf.opnmf_core import opnmf
participant_tsv="path_to_participant_tsv"
output_dir = "path_output_dir"
tissue_binary_mask = "path_to_tissue_mask"
num_component_min = 2
num_component_max = 60
n_threads = 8
verbose = True
opnmf(participant_tsv, output_dir, tissue_binary_mask, num_component_min, num_component_max, n_threads=n_threads, verbose=verbose)

Alternatively, if you have a large N, you can train the model with the SOPNMF model to overcome the memory limitations:

from sopnmf.opnmf_core import sopnmf
participant_tsv="path_to_participant_tsv"
participant_tsv_max_memory="path_to_participant_tsv_with_max_N"
output_dir = "path_output_dir"
tissue_binary_mask = "path_to_tissue_mask"
num_component_min = 2
num_component_max = 60
n_threads = 8
verbose = True
sopnmf(participant_tsv, output_dir, tissue_binary_mask, num_component_min, num_component_max, n_threads=n_threads, verbose=verbose)

Second, you need to apply the trained model to the training data for post-hoc analyses:

from sopnmf.opnmf_post import apply_to_training
output_dir = "path_output_dir"
tissue_binary_mask = "path_to_tissue_mask"
num_component = 2
apply_to_training(output_dir, num_component, tissue_binary_mask, verbose=True)

Last, you may also apply the trained model to unseen test data:

from sopnmf.opnmf_post import apply_to_test
participant_tsv="path_to_participant_tsv"
tissue_binary_mask = "path_to_tissue_mask"
num_component = 2
output_dir = "path_output_dir"
apply_to_test(output_dir, num_component, tissue_binary_mask, participant_tsv, verbose=True)

Citing this work

Wen, J. et al., Mega-analysis of brain structural covariance, genetics, and clinical phenotypes. - In review

Sotiras, A., Resnick, S.M. and Davatzikos, C., 2015. Finding imaging patterns of structural covariance via non-negative matrix factorization. Neuroimage, 108, pp.1-16. doi:10.1016/j.neuroimage.2014.11.045

Publications using SOPNMF

Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G.B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D.B. and Pigoni, A., 2022. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Medical Image Analysis, 75, p.102304. - Link