SpatialCCCbench: Standardized Metrics for the Systematic Evaluation of Spatial Cell-Cell Communication Methods
Dai, W.
Show abstract
Spatial transcriptomics (ST) enables transcriptome profiling with preserved spatial context, providing spatial dimensions that are essential for understanding complex intercellular signals in tissue architecture. ST-based CCC tools integrate spatial and molecular information to decipher intercellular interactions from a spatially informed perspective. Despite the rapid evolution of many CCC computational tools, a systematic assessment of their performance in handling ST-specific heterogeneity, utilizing spatial information efficiently, and robustness against technical or biological noise is still lacking. To address this gap, SpatialCCCbench incorporates classification accuracy, spatial signal features, robustness, and user-friendliness, aiming to guide the selection of optimal CCC inference tools across diverse spatial biology contexts. SpatialCCCbench systematically evaluates the scenario-specific applicability of ST-based CCC tools. It helps users select tools according to their analytical objectives and provides a practical benchmark for future method development. HighlightsO_LIEstablished a multi-dimensional benchmark suite to evaluate cell-cell communication (CCC) inference methods in spatial transcriptomics. C_LIO_LICharacterized the spatial patterns of CCC signals across diverse tissues using spatial autocorrelation and local diversity analysis. C_LIO_LISystematically assessed the robustness of CCC inference tools across six common experimental noise scenarios in spatial transcriptomics. C_LIO_LIIntegrated boundary-feature analysis, a mechanistically important component for biological interpretation, to uncover spatial preferences and algorithmic biases in CCC methods. C_LIO_LIProvided guidelines to assist in the selection of optimal CCC inference tools tailored to various spatial biology contexts. C_LI Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/724475v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@12bbc6aorg.highwire.dtl.DTLVardef@5eee6borg.highwire.dtl.DTLVardef@76d8f2org.highwire.dtl.DTLVardef@9d077e_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 7 journals account for 50% of the predicted probability mass.