cell-communication

Infer cell-cell communication by processing annotated scRNA-seq data and a ligand-receptor resource to calculate interaction scores, generating interaction tables and sender-receiver summaries for downstream interpretation.

Spatial transcriptomics data is supported for cell signaling inference. The workflow computes interaction scores for spatial neighborhoods to generate pathway-level visualizations and sender-receiver summaries.

Generating cell-cell interaction networks requires an annotated single-cell or spatial object, a ligand-receptor reference resource, and metadata to initiate the Python-based workflow using pandas for data handling.

QC artifacts are addressed during ligand-receptor signaling inference by applying cautious interpretation to the generated interaction networks and sender-receiver summaries, ensuring downstream analysis reliability.

Network visualization is supported for cell-cell interactions. The workflow generates pathway-style visualizations and interaction tables from the computed ligand-receptor signaling scores.

Summarize sender-receiver cell pairs by computing interaction scores from annotated scRNA-seq or spatial data, producing interaction tables and pathway-level views for downstream interpretation.