Abstract
<title>Abstract</title> <p>Spatial transcriptomics provides a direct view of tissue architecture, but ligand-receptor (LR) communication is often inferred with spatial neighborhoods or distance kernels that are shared across many LR pairs. This is limiting because contact-dependent, paracrine and chemokine-like niche programs can require distinct effective spatial supports. We present ScaleComm, a graph-wavelet framework for event-level spatial cell-cell communication inference. ScaleComm uses a tissue graph as a multi-scale scaffold, learns an LR-specific scale mixture and reports a dataset-specific scale profile for each LR pair, while scoring candidate sender-receiver-LR events with joint sender-receiver context features. In a mechanism-aware semi-synthetic benchmark, ScaleComm improved the balance between LR recovery, event localization and suppression of mechanism-mismatched decoys relative to representative spatial and cell-type-level baselines. Across tumor, inflammatory and lymphoid spatial datasets, ScaleComm produced localized candidate events whose receiver maps were supported by downstream response signatures and tissue-region enrichment, including TGFB1-TGFBR2 remodeling in colorectal cancer and CXCL13-CXCR5 enrichment in germinal-center regions. ScaleComm provides inspectable scale profiles, spatially localized candidate events and response-based validation readouts for studying LR communication in tissue context.</p>