From 4908775fe746c113b22bb4eb8ca38c2cf1561e43 Mon Sep 17 00:00:00 2001 From: Kalin Nonchev Date: Wed, 15 Jul 2026 14:19:27 +0300 Subject: [PATCH] Add AESTETIK to the ecosystem AESTETIK is a convolutional autoencoder that learns multi-modal spot representations from spatial transcriptomics (transcriptomics + morphology + spatial topology) on AnnData. MIT-licensed, released on PyPI, and tested in CI. Co-Authored-By: Claude Fable 5 --- packages/AESTETIK/meta.yaml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 packages/AESTETIK/meta.yaml diff --git a/packages/AESTETIK/meta.yaml b/packages/AESTETIK/meta.yaml new file mode 100644 index 0000000..9d5b25e --- /dev/null +++ b/packages/AESTETIK/meta.yaml @@ -0,0 +1,29 @@ +name: AESTETIK +description: | + Convolutional autoencoder that learns spot representations from spatial + transcriptomics by jointly integrating transcriptomics, morphology (H&E), and + spatial-neighborhood topology. The learned embeddings support downstream tasks + such as spatial-domain clustering and multi-modal analysis. +project_home: https://github.com/ratschlab/aestetik +documentation_home: https://github.com/ratschlab/aestetik#readme +tutorials_home: https://github.com/ratschlab/aestetik/tree/main/example +publications: + - 10.1093/bioinformatics/btag316 + - 10.1101/2024.06.04.24308256 +install: + pypi: aestetik +tags: + - spatial-omics + - spatial-transcriptomics + - representation-learning + - autoencoder + - multimodal + - computational-pathology + - deep learning + - pytorch +license: MIT +version: v0.3.1 +contact: + - KalinNonchev +test_command: pip install '.[test-all]' && pytest -m "not slow" +category: ecosystem