T-cell classification

Citation

This repository contains code and data for the manuscript:

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks. Zijie J Wang, Alex J Walsh, Melissa C Skala, Anthony Gitter. Journal of Biophotonics, 13:3, 2020.

Description

Directory/File	Renderer	Description
images		Directory containing T-cell images
plots		Directory containing supplementary figures
resource		Directory containing supplementary training files and CellProfiler pipelines
image_processing.ipynb		Notebook for image cropping, filtering and mask measurement
frequency_classifier.ipynb		Notebook for a trivial frequency classifier
logistic_regression.ipynb		Notebook for fitting three logistic regression models with different feature vectors
simple_neural_network.ipynb		Notebook for training a one-layer fully connected neural network
simple_cnn_lenet.ipynb		Notebook for end-to-end training CNN LeNet
transfer_learning.ipynb		Notebook for fine-tuning layers of pre-trained Inception v3

• Nbviewer renders these notebooks as static HTML web pages.
• Binder hosts an executable environment for notebooks.
• Three conda environment files are provided. environment.yml and environment-windows.yml include main dependencies with major versions for macOS/Linux and Windows respectively; environment-complete.yml provides a complete list of all packages and versions.
• You can use conda env create -f {environment.yml|environment-windows.yml|environment-complete.yml} to install dependencies.
• On Windows, the R package ggpubr must be installed separately after creating the conda environment with the command Rscript -e "install.packages('ggpubr')".

License

The software is available under the BSD 3-Clause Clear License.