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Compute spatial CCC graphs over a list of LR pairs

Source: R/spatial_ccc.R
compute_spatial_ccc_graph_list.Rd

Compute spatial CCC graphs over a list of LR pairs

Usage

compute_spatial_ccc_graph_list(
  spe,
  assay_name = "logcounts",
  LRdb,
  expression_min_prop = 0.05,
  spot_dist_cutoff = 1.5,
  LRscore_cutoff = 0.5,
  workers = 1
)

Arguments

spe

SpatialExperiment object

assay_name

assay name in string

LRdb

LRdb in data.frame (see get_LRdb())

expression_min_prop

minimum proportion of samples with non-zero expression value (default: 0.05)

spot_dist_cutoff

cutoff value for norm.d in spot distances. Default cutoff is 1.5 and it comes from sqrt(3) ~ 1.73, based on how Visium spot-array is arranged, so by default, only computing nearest neighbors.

LRscore_cutoff

minimum LRscore to keep

workers

the number of processes to be used for parallel processing

Value

a list of spatial CCC graphs

graph metrics

  1. For overall graph,

    • graph_n_nodes,

    • graph_n_edges

    • graph_component_count

    • graph_motif_count

    • graph_diameter

    • graph_un_diameter

    • graph_mean_dist

    • graph_circuit_rank = graph_n_edges - graph_n_nodes + graph_component_count

    • graph_reciprocity

    • graph_clique_num (sp_ccc_graph assumed as undirected)

    • graph_clique_count (sp_ccc_graph assumed as undirected)

  2. For each sub-graph (after group_components())

    • group_n_nodes

    • group_n_edges

    • group_adhesion

    • group_motif_count

    • group_diameter

    • group_un_diameter

    • group_mean_dist

    • group_girth

    • group_circuit_rank

    • group_reciprocity