dpaysan/nmco-image-features

Nuclear Morphology and Chromatin Organization Features

NMCO-Image-Features

The packing of the genome within the nucleus informs the cellular state. High resolution images of DNA as visualised using a fluorescent microscope is a convenient tool to characterize 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.

Documentation is available here

Installation

The current implementation has been developed in Python 3 and tested in Ubuntu 18.0

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/NMCO-Image-Features.git

Then install requirements and run the setup from the repository directory

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

Simple example

#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)

How to cite

@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}
}