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

ABSTRACT: 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

SUBMITTER: Cesaro G 

PROVIDER: S-EPMC12204404 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature