Biology subjects

Brunn, N.

Publications and source records attributed to Brunn, N..

4 recordsLinked to original sources

bioRxiv2026

Coding agents author interpretable single-cell embedding models from the literature

The single-cell literature catalogs cell states as validated marker-gene programs -- a sparse, compositional prior. Conventional embedding methods do not leverage this prior and learn cell-state structure de novo from the expression matrix, producing dense dimensions needing post-hoc interpretation and batch correction. Here we show coding agents can author single-cell embedding models directly from the literature. Given a scenario that focuses this literature lens on a chosen biological subdomain, the agent edits a structured Python template, curating named, literature-cited gene programs and composing them into axes, without a gene-set database, training, or sight of the data. Across mouse and human tissues these zero-shot embeddings are competitive in biological quality with conventional, foundation-model, and program-informed baselines, batch-robust by construction and reproducible across runs, complementing data-driven embeddings. Because each dimension is a named, cited gene program, the embedding is interpretable and auditable, and its composable axes can be steered into a developmental tree.

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bioRxiv2026

Mapping spatial cell-cell communication programs by tailoring chains of cells for transformer neural networks

Recent advances in spatial transcriptomics and computational modeling enable the study of cellular interactions in situ. However, existing methods quantify ligand-receptor activity pairwise or between predefined cell groups, yielding overlapping signals and limited ability to summarize concurrent interactions into programs while localizing communication hotspots. We introduce scCChain, a transformer-based framework that integrates ligand-receptor activity into spatially resolved communication programs and localizes hotspots at spot and single-cell resolution. scCChain first derives candidate programs using structured dimensionality reduction. Subsequently, it samples programspecific communication chains by linking transcriptionally similar sender cells to candidate receivers via weighted random walks on a distance-informed cell graph, borrowing signal from similar neighbors. Transformer-based modeling then scores chains to prioritize communication programs and pinpoint hotspots across the tissue. Applied to human breast cancer spatial transcriptomics data at spot and single-cell resolution, scCChain supports both exploratory communication program discovery and targeted analysis of user-specified ligand-receptor pairs. In spot-level data, it prioritizes a tumor-associated program enriched for pro-angiogenic signaling that localizes to invasive regions. In imaging-based data, it highlights CXCL12-CXCR4 communication hotspots at cellular resolution. Here, we demonstrate that chain-based transformer modeling enables interpretable discovery and mapping of biological meaningful spatial communication programs within complex tissues.

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bioRxiv2024

Sparse dimensionality reduction for analyzing single-cell-resolved interactions

SummarySeveral approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance downstream analyses, we present an end-to-end dimensionality reduction workflow, specifically tailored for single-cell cell-cell interaction data. In particular, we demonstrate that sparse dimensionality reduction can pinpoint specific ligand-receptor interactions in relation to clusters of cell pairs. For sparse dimensionality reduction, we focus on the Boosting Autoencoder approach (BAE). Overall, we provide a comprehensive workflow, including result visualization, that simplifies the analysis of interaction patterns in cell pairs. This is supported by a Jupyter notebook that can readily be adapted to different datasets. Availability and implementationhttps://github.com/NiklasBrunn/Sparse-dimension-reduction Contactniklas.brunn@uniklinik-freiburg.de Supplementary material...

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bioRxiv2024

Infusing structural assumptions into dimension reduction for single-cell RNA sequencing data to identify small gene sets

Dimensionality reduction greatly facilitates the exploration of cellular heterogeneity in single-cell RNA sequencing data. While most of such approaches are data-driven, it can be useful to incorporate biologically plausible assumptions about the underlying structure or the experimental design. We propose the boosting autoencoder (BAE) approach, which combines the advantages of unsupervised deep learning for dimensionality reduction and boosting for formalizing assumptions. Specifically, our approach selects small sets of genes that explain latent dimensions. As illustrative applications, we explore the diversity of neural cell identities and temporal patterns of embryonic development.

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