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.