CHACAM: a cell–cell interaction–guided hierarchical attention model for high-precision cell identity annotation of scRNA-seq data in early C. elegans embryogenesis

Abstract Accurate cell identity annotation is essential for reconstructing cell lineages and gene-regulatory networks in embryogenesis. However, in Caenorhabditis elegans ( C. elegans ) single-cell RNA-sequencing (scRNA-seq) atlases, closely related sub-lineage cells often have near-identical transcriptomes, leading to merged labels and limiting downstream analyses. We present CHACAM (Cell–Cell Interaction–guided Hierarchical Attention-based Cell Allocation Model), a supervised machine learning framework that integrates gene expression, a curated C. elegans ligand–receptor (L–R) interaction database, and a high-resolution cell contact map to refine cell annotations. CHACAM learns interpretable “interaction signatures” that distinguish contacting vs. non-contacting neighbors and applies a triangulation strategy using reference cells to resolve ambiguous sister identities. Applied to the Tintori and Cole–Yanai embryo atlases, CHACAM achieves near-perfect annotation accuracy, resolving longstanding ambiguities of the AB lineage and other lineages. The model also identifies new discriminative marker genes and provides mechanistic hypotheses linking contact-specific signaling to fate specification.