Sparse dimensionality reduction for analyzing single-cell-resolved interactions

TL;DR

The paper introduces a workflow using sparse dimensionality reduction to analyze cell-cell interactions from single-cell data, focusing on ligand-receptor interactions.

Contribution

The novel contribution is an end-to-end workflow using Boosting Autoencoder for sparse dimensionality reduction in single-cell interaction analysis.

Findings

Sparse dimensionality reduction highlights specific ligand-receptor interactions in cell pair clusters.

The workflow includes visualization tools and is implemented in a Jupyter notebook for adaptability.

Abstract

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.

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