Sparse dimensionality reduction for analyzing single-cell-resolved interactions

Several 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. 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.

https://github.com/NiklasBrunn/Sparse-dimension-reduction.