Niklas Brunn

This paper introduces coding agents that generate interpretable single-cell embeddings from literature-based gene programs without training data or databases.

The paper introduces scCChain, a transformer-based framework for mapping spatial cell-cell communication programs in spatial transcriptomics data, identifying hotspots at single-cell and spot resolution.

The paper introduces a sparse dimensionality reduction workflow for analyzing single-cell cell-cell interactions using the Boosting Autoencoder approach, with a Jupyter notebook for implementation.

The paper introduces a boosting autoencoder method to incorporate structural assumptions into dimensionality reduction for single-cell RNA sequencing data, identifying small gene sets that explain latent dimensions.