scConnect: a method for exploratory analysis of cell-cell communication based on single-cell RNA-sequencing data

Cell to cell communication is critical for all multicellular organisms, and single-cell sequencing facilitates the construction of full connectivity graphs between cell types in tissues. Such complex data structures demand novel analysis methods and tools for exploratory analysis.

We propose a method to predict the putative ligand-receptor interactions between cell types from single-cell RNA-sequencing data. This is achieved by inferring and incorporating interactions in a multi-directional graph, thereby enabling contextual exploratory analysis. We demonstrate that our approach can detect common and specific interactions between cell types in mouse brain and human tumors, and that these interactions fit with expected outcomes. These interactions also include predictions made with molecular ligands integrating information from several types of genes necessary for ligand production and transport. Our implementation is general and can be appended to any transcriptome analysis pipeline to provide unbiased hypothesis generation regarding ligand to receptor interactions between cell populations or for network analysis in silico.

scConnect is open source and available as a Python package at https://github.com/JonETJakobsson/scConnect. scConnect is directly compatible with Scanpy scRNA-sequencing pipelines.

Supplementary data are available at Bioinformatics online.