spatCellCellcomSpots

Source: R/spatial_interaction_spot.R

Spatial Cell-Cell communication scores based on spatial expression of interacting cells at spots resolution

spatCellCellcomSpots(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  ave_celltype_exp,
  spatial_network_name = "Delaunay_network",
  spat_enr_name = "DWLS",
  cluster_column = "cell_ID",
  random_iter = 1000,
  feature_set_1,
  feature_set_2,
  min_observations = 2,
  expression_values = c("normalized", "scaled", "custom"),
  detailed = FALSE,
  adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
    "none"),
  adjust_target = c("features", "cells"),
  do_parallel = TRUE,
  cores = NA,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = c("a little", "a lot", "none")
)

Arguments

gobject

giotto object to use

spat_unit

spatial unit (e.g. 'cell')

feat_type

feature type (e.g. 'rna')

ave_celltype_exp

Matrix with average expression per cell type

spatial_network_name

spatial network to use for identifying interacting cells

spat_enr_name

name of spatial enrichment containing DWLS results. Default = "DWLS"

cluster_column

cluster column with cell type information

random_iter

number of iterations

feature_set_1

first specific feature set from feature pairs

feature_set_2

second specific feature set from feature pairs

min_observations

minimum number of interactions needed to be considered

expression_values

(e.g. 'normalized', 'scaled', 'custom')

detailed

provide more detailed information (random variance and z-score)

adjust_method

which method to adjust p-values

adjust_target

adjust multiple hypotheses at the cell or feature level

do_parallel

run calculations in parallel with mclapply

cores

number of cores to use if do_parallel = TRUE

set_seed

set a seed for reproducibility

seed_number

seed number

verbose

verbose (e.g. 'a little', 'a lot', 'none')

Value

Cell-Cell communication scores for feature pairs based on spatial interaction

Details

Statistical framework to identify if pairs of features (such as ligand-receptor combinations) are expressed at higher levels than expected based on a reshuffled null distribution of feature expression values in cells that are spatially in proximity to each other.

    • LR_comb:Pair of ligand and receptor

    • lig_cell_type: cell type to assess expression level of ligand

    • lig_expr: average expression residual(observed - DWLS_predicted) of ligand in lig_cell_type

    • ligand: ligand name

    • rec_cell_type: cell type to assess expression level of receptor

    • rec_expr: average expression residual(observed - DWLS_predicted) of receptor in rec_cell_type

    • receptor: receptor name

    • LR_expr: combined average ligand and receptor expression residual

    • lig_nr: total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type

    • rec_nr: total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type

    • rand_expr: average combined ligand and receptor expression residual from random spatial permutations

    • av_diff: average difference between LR_expr and rand_expr over all random spatial permutations

    • sd_diff: (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations

    • z_score: (optional) z-score

    • log2fc: LR_expr - rand_expr

    • pvalue: p-value

    • LR_cell_comb: cell type pair combination

    • p.adj: adjusted p-value

    • PI: significanc score: log2fc \* -log10(p.adj)