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.
- [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.
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.
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
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.