Package index
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prepare_input_files() - Build RaCInG input files from raw count data
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compute_racing_kernel() - Run the full kernel-based RaCInG workflow
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compute_racing_montecarlo() - Run the full Monte Carlo RaCInG workflow
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compute_kernel() - Compute the RaCInG kernel for one or more patients
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compute_kernel_features() - Derive communication features from a kernel
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calculateDirect() - Calculate direct communication features from a kernel
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calculateWedges() - Calculate wedge features from a kernel
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computeTriangles() - Compute triangle features from kernel matrices
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computeGSCC() - Compute GSCC features from kernel matrices
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countWedges() - Count wedges across Monte Carlo graph simulations
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countTrustTriangles() - Count trust triangles across Monte Carlo graph simulations
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countCycleTriangles() - Count cycle triangles across Monte Carlo graph simulations
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countDirect() - Count direct edges across Monte Carlo graph simulations
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countGSCC() - Count GSCC contributions across Monte Carlo graph simulations
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countAllTypes() - Extract multiple communication feature types from the same simulated graphs
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model1() - Generate a single RaCInG graph realization
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runSim() - Run Monte Carlo simulations for one or more patients
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getGSCCAnalytically() - Legacy GSCC helper
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computeLRContributions() - Decompose a Direct communication score into per-ligand-receptor-pair contributions
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computeWedgeLRContributions() - Decompose a Wedge's two hub paths into per-edge ligand-receptor contributions
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computeTriangleLRContributions() - Decompose a Triangle's edges into per-edge ligand-receptor contributions
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plotLRContributions() - Dot plot of top ligand-receptor pair contributions (LIANA-style)
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createCellLigList() - Read a cell-to-ligand compatibility matrix
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createCellRecList() - Read a cell-to-receptor compatibility matrix
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createCellTypeDistr() - Read and normalize cell-type abundance estimates
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createInteractionDistr() - Read ligand-receptor interaction probabilities
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Read_Lig_Rec_Interaction() - Read a ligand-receptor sign matrix
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genRandomCellTypeList() - Sample cell-type labels for graph vertices
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EdgetoAdj() - Convert an edge list to a sparse adjacency matrix
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EdgetoAdj_No_loop() - Convert an edge list to a sparse adjacency matrix without self-loops
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Count_Types() - Count graph motifs by cell-type combination
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Trust_Triangles() - Enumerate outward trust triangles
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Cycle_Triangles() - Enumerate directed cycle triangles
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Wedges() - Enumerate wedges in a directed graph
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GSCC() - Extract the giant strongly connected component
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skcm_example - SKCM melanoma example input for RaCInG
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wilcox_group_test() - Run Wilcoxon tests across network features
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top_features_plot() - Ranked bar plot of top up/down features from Wilcoxon results
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correlate_features_with_score() - Correlate network features with an external score
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correlation_plot() - Rainfall plot of feature-score correlations
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Read_Sim_Output() - Read a RaCInG simulation output file
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compute_results_processing() - Convert raw simulation outputs into feature matrices