This code is part of the paper "Representation learning for multi-modal spatially resolved transcriptomics data".
Authors: Kalin Nonchev, Sonali Andani, Joanna Ficek-Pascual, Marta Nowak, Bettina Sobottka, Tumor Profiler Consortium, Viktor Hendrik Koelzer, and Gunnar Rätsch
The preprint is available here.
You can find the paper analysis code here.
You can find the AESTETIK code and tutorial on how to use it here.
We adapted the simulation approach suggested in [5] by introducing spatial structure in the experiment. Briefly, relying on simulated ground truth labels, we simulate transcriptomics and morphology modalities, allowing partial observation of true clusters within each modality individually. However, combining both modalities enables the identification of all clusters. Spatial coordinates are incorporated by sorting the ground truth in spatial space.
Please take a look at our example notebook to get started.
In case you found our work useful, please consider citing us:
@article{nonchev2024representation,
title={Representation learning for multi-modal spatially resolved transcriptomics data},
author={Nonchev, Kalin and Andani, Sonali and Ficek-Pascual, Joanna and Nowak, Marta and Sobottka, Bettina and Tumor Profiler Consortium and Koelzer, Viktor Hendrik and Raetsch, Gunnar},
journal={medRxiv},
pages={2024--06},
year={2024},
publisher={Cold Spring Harbor Laboratory Press}
}
In case, you have questions, please get in touch with Kalin Nonchev.