Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.

ABSTRACT: From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synt

SUBMITTER: Almet AA 

PROVIDER: S-EPMC11466815 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature