MMCCI: Multimodal Cell-Cell Interaction Integrative Analysis of Single Cell and Spatial Transcriptomics Data

Cell-cell interaction (CCI) analyses are becoming an indispensable discovery tool for cutting-edge single cell and spatial omics technologies, identifying ligand-receptor (LR) targets in intercellular communications at the molecular, cellular, and microenvironment levels. Different transcriptional-based modalities can add complementary information and provide independent validation of a CCI, but so far no robust methods exist to integrate CCI results together. To address this, we have developed a statistical and computational pipeline, Multimodal CCI (MMCCI), implemented in an open-source Python package, which integrates, analyzes, and visualizes multiple LR-cell-type CCI networks across multiple samples of the same modality as well as between multiple modalities. MMCCI implements new and in-depth downstream analyses, including comparisons between biological conditions, network and interaction clustering, sender-receiver interaction querying, and biological pathway analyses. We applied MMCCI to statistically integrate CCIs in our spatial transcriptomics datasets of aging mouse brains (from 10X Visium and BGI STOmics) and melanoma (10X Visium, 10X Xenium and NanoString CosMx) and identified biologically meaningful interactions, piecing together the complex interactions and pathways involved in normal physiology and disease at the molecular level with the statistical confidence of using large, multimodal datasets. With MMCCI, the community will have access to a valuable tool for harnessing the power of multimodal single cell and spatial transcriptomics. MMCCI source code and documentation are available at: . ### Competing Interest Statement The authors have declared no competing interest.