aestetik 0.1.0

AESTETIK: AutoEncoder for Spatial Transcriptomics Expression with Topology and Image Knowledge

AESTETIK: AutoEncoder for Spatial Transcriptomics Expression with Topology and Image Knowledge

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.

News

• [08.2024] AESTETIK secured the 1st place at the Mammoth International Contest On Omics Sciences in Europe 2024 organized by China National GeneBank, BGI Genomics, MGI and CODATA link.

Changelog

NEW version (June 2025)

• UPDATE: Rewrote AESTETIK using the Lightning framework for improved modularity
• Added: New fit()/predict() API
• Added: Support for processing multiple samples at once
• Removed: Multiple old methods and parameters in AESTETIK

See full changelog for more details.

Do you want to gain a multi-modal understanding of key biological processes through spatial transcriptomics?

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.

Setup

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

NB: Please ensure you have installed pyvips depending on your machine's requirements. We suggest installing pyvips through conda:

conda install conda-forge::pyvips

Getting Started

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

Here, another example notebook with simulated spatial transcriptomics data.

Citation

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.

Contact

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