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bioRxiv · 10.1101/2024.12.01.626228

Sparse dimensionality reduction for analyzing single-cell-resolved interactions

Brunn, N.·Hackenberg, M.·Vogel, T.·Binder, H.
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Abstract

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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Brunn, N., Hackenberg, M., Vogel, T., Binder, H.. 2024-12-05. Sparse dimensionality reduction for analyzing single-cell-resolved interactions. https://doi.org/10.1101/2024.12.01.626228

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