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README

This repository contains jupyter notebooks and code developed to classify 2D high-content screening images based on texture and other features into 3 classes.

Data

  • Coacervates data from 96-well plate, where each well has different concentrations of 2 proteins of interest
  • The different concentrations lead to three categories of images: droplets, aggregates, empty.
    • Empty images look just like background
    • Droplet images show transparent droplets (just circles) of various sizes
    • Aggregates - textured image, clumps of protein, no clear structure

Analysis goal

Classify images automatically into one of the 3 categories.

Final workflow

  • Based on python using libraries scikit-learn, mahotas, scikit-image, tifffile.
  • Workflow
    • functions.py: contains all the helper functions
    • 1_read_images.ipynb: read and save images for training - on laptop
    • 2_train_save_classifier.ipynb: train classifier - on cluster
    • 3_do_prediction.ipynb: apply model and get predictions - on laptop
    • README_Python_Installation.pdf
    • imageclassification.yml: env file
  • Additionally
    • explorations: contains additional notebooks from various explorations using deeplearning, cnn and transfer-learning
    • 1A_read_images_augment.ipynb for data augmentation part

This project was original done in collaboration with Rudrarup Bose, PhD at Tang lab (Nov 2021, MPI-CBG, Dresden). The code is published with permission.