Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data

Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell-cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell-cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects.