ratschlab/aestetik
AESTETIK: Convolutional autoencoder for learning spot representations from spatial transcriptomics and morphology data
This tool helps biologists and medical researchers understand complex biological processes by combining different types of data from spatial transcriptomics. It takes in spatial transcriptomics data and corresponding morphology images to produce 'spot representations' that reveal how gene expression and tissue structure are related. Scientists studying disease mechanisms or cell biology would use this to get a more integrated view of their samples.
Available on PyPI.
Use this if you need to integrate and learn meaningful representations from both spatial transcriptomics and morphology (histology) image data to analyze tissue samples.
Not ideal if you are only working with single-cell RNA sequencing data without spatial information or morphology images.
Stars
20
Forks
4
Language
Python
License
MIT
Last pushed
Mar 09, 2026
Commits (30d)
0
Dependencies
15
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