s3cima 0.1.13

Supervised Spatial Single-Cell Image Analysis for identification of disease associated cell type composition in the tissue microenvironment

S3-CIMA

Supervised Spatial Single-Cell Image Analysis for identification of disease associated cell type composition in the tissue microenvironment

S3-CIMA implements a weakly supervised CNN model to identify cell subsets whose frequency distinguishes the considered phenotype labels (i.e., disease associated conditions). The model is adopted from the CellCNN model (Arvaniti and Claassen, 2017), comprising a single layer CNN, a pooling layer and a classification or regression output, and using groups of cell expression profiles (multi-cell inputs) as input.

Installation

S3-CIMA is available on PyPI and can be installed using the command:

pip install s3cima

If this does not work, you can clone the repo :

git clone https://github.com/claassenlab/S3-CIMA.git

and run the functions in a conda environment with the following packages :

conda create --name sc3cima 
conda activate s3cima
conda install python=3.11 numpy pandas scipy pytorch scikit-learn tqdm matplotlib plotly

Usage

Examples are provided in cima_example.ipynb. Further guidance and documentation to be added soon.

run_scima log file

The model training parameters and outputs is written in a log file including:

• Important parameters such as K, ncell and anchor celltype

• Balanced accuracy score on the train/validation/test set

plot_results output:

Plotting not yet added ! Will be done very soon.

Citation

If you use S3-CIMA in your research, please cite our paper:

Sepideh Babaei, Jonathan Christ, Vivek Sehra, Ahmad Makky, Mohammed Zidane, Kilian Wistuba-Hamprecht, Christian M. Schürch, Manfred Claassen, S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue, Patterns, Volume 4, Issue 9, 2023, 100829, ISSN 2666-3899, https://doi.org/10.1016/j.patter.2023.100829.

License

S3-CIMA is released under the MIT License. See the LICENSE file for more details.