Here, we present scifAI-notebooks which are used to analyze the synapse formation data including feature extraction, statistical analysis and machine learning methods. Each folder includes a jupyter notebook that is used for creation of the figures for the manuscript.
Note: this repository only includes the jupyter notebooks on the synapse formation data (to be published). For following on how this scifAI package is works, please refer to https://github.com/marrlab/scifAI
To cite this work, you would need to cite the preprint and the dataset:
preprint: scifAI: Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies Sayedali Shetab Boushehri, Katharina Essig, Nikolaos-Kosmas Chlis, Sylvia Herter, Marina Bacac, Fabian J Theis, Elke Glasmacher, Carsten Marr, Fabian Schmich bioRxiv 2022.10.24.513494; doi: https://doi.org/10.1101/2022.10.24.513494
dataset: Essig, Katharina et al. (2022), An imaging flow cytometry dataset for profiling the immunological synapse of therapeutic antibodies, Dryad, Dataset, https://doi.org/10.5061/dryad.ht76hdrk7