This model 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.

We introduce AESTETIK, a convolutional autoencoder model. It jointly integrates transcriptomics and morphology information, on a spot level, and topology, on a neighborhood level, to learn accurate spot representations that capture biological complexity.

Fig. 1 AESTETIK integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. A: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. B: Workflow of AESTETIK. Initially, the transcriptomics and morphology spot representations are preprocessed. Next, a dimensionality reduction technique (e.g., PCA) is applied. Subsequently, the processed spot representations are clustered separately to acquire labels required for the multi-triplet loss. Afterwards, the modality-specific representations are fused through concatenation and the grid per spot is built. This is used as an input for the autoencoder. Lastly, the spatial-, transcriptomics-, and morphology-informed spot representations are obtained and used for downstream tasks such as clustering, morphology analysis, etc.

We can install aestetik directly through pip.

pip install aestetik

We can also create a conda environment with the required packages.

conda env create --file=environment.yaml

We can also install aestetik offline.

git clone https://github.com/ratschlab/aestetik
cd aestetik
python setup.py install

conda install conda-forge::pyvips

Please take a look at our example to get started with AESTETIK.

Here, another example notebook with simulated spatial transcriptomics data.

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}
}

The code for reproducing the paper results can be found here.

In case, you have questions, please get in touch with Kalin Nonchev.