Since, the packing of the genome within the nucleus informs cellular state, nuclear morphology and chromatin organization(NMCO) features hold biologically meaningful information. High resolution images of DNA as visualised using a fluorescent microscope is a convenient tool to characterize such DNA organization.

This package aims to provide an exhaustive set of interpretable morphometric and texture features for every single nucleus following segmentation from a 2D single channel image. Below is a brief overview of the features used

Documentation is being updated and will be made available here

The list of features and their description can be found in the file "chrometric_feature_description.csv"

Illustration of feature extraction can also be checked here

The current implementation has been developed in Python 3.

In order to avoid any changes to the local packages, install in a virtual environment (optional).

   $ conda create --name nmco python
   $ conda activate nmco

To clone the repository run the following from the terminal.

   $ git clone https://github.com/GVS-Lab/chrometrics.git

Then install requirements and run the setup from the repository directory

   $ pip install -r requirements.txt
   $ python setup.py install

#import libraries
import os
from nmco.utils.run_nuclear_feature_extraction import run_nuclear_chromatin_feat_ext


# initialising paths
labelled_image_path = os.path.join(os.path.dirname(os.getcwd()),'example_data/nuc_labels.tif')
raw_image_path = os.path.join(os.path.dirname(os.getcwd()),'example_data/raw_image.tif')
feature_path = os.path.join(os.path.dirname(os.getcwd()),'example_data/')

# For a quick extraction of all available features for all labelled nuclei given a segmented image with default parameters
features = run_nuclear_chromatin_feat_ext(raw_image_path,labelled_image_path,feature_path)

Alternatively, use the CLI version using default parameters as follows.

python measure_nmco_features.py --rawdir <path/to/image> --datadir <path/to/labelled_image> --savedir <path/to/output/folder>

@article{venkatachalapathy2020multivariate,
  title={Multivariate analysis reveals activation-primed fibroblast geometric states in engineered 3D tumor microenvironments},
  author={Venkatachalapathy, Saradha and Jokhun, Doorgesh Sharma and Shivashankar, GV},
  journal={Molecular biology of the cell},
  volume={31},
  number={8},
  pages={803--812},
  year={2020},
  publisher={Am Soc Cell Biol}
}