Spatial Cell-Cell communication scores based on spatial expression of interacting cells
spatCellCellcom(
gobject,
feat_type = NULL,
spat_unit = NULL,
spatial_network_name = "Delaunay_network",
cluster_column = NULL,
random_iter = 1000,
feat_set_1,
feat_set_2,
gene_set_1 = NULL,
gene_set_2 = NULL,
log2FC_addendum = 0.1,
min_observations = 2,
detailed = FALSE,
adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
"none"),
adjust_target = c("feats", "cells"),
do_parallel = TRUE,
cores = NA,
set_seed = TRUE,
seed_number = 1234,
verbose = c("a little", "a lot", "none")
)
specificCellCellcommunicationScores(
gobject,
feat_type = NULL,
spat_unit = NULL,
spatial_network_name = "Delaunay_network",
cluster_column = NULL,
random_iter = 100,
cell_type_1 = NULL,
cell_type_2 = NULL,
feat_set_1,
feat_set_2,
gene_set_1 = NULL,
gene_set_2 = NULL,
log2FC_addendum = 0.1,
min_observations = 2,
detailed = FALSE,
adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
"none"),
adjust_target = c("feats", "cells"),
set_seed = FALSE,
seed_number = 1234,
verbose = TRUE
)giotto object to use
feature type
spatial unit
spatial network to use for identifying interacting cells
cluster column with cell type information
number of iterations
first specific feature set from feature pairs
second specific feature set from feature pairs
deprecated, use feat_set_1
deprecated, use feat_set_2
addendum to add when calculating log2FC
minimum number of interactions needed to be considered
provide more detailed information (random variance and z-score)
which method to adjust p-values
adjust multiple hypotheses at the cell or feature level
run calculations in parallel with mclapply
number of cores to use if do_parallel = TRUE
set a seed for reproducibility
seed number
verbose
character. First cell type
character. Second cell type
Cell-Cell communication scores for feature pairs based on spatial interaction
Statistical framework to identify if pairs of genes (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 of ligand in lig_cell_type
ligand: ligand name
rec_cell_type: cell type to assess expression level of receptor
rec_expr: average expression of receptor in rec_cell_type
receptor: receptor name
LR_expr: combined average ligand and receptor expression
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 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: log2 fold-change (LR_expr/rand_expr)
pvalue: p-value
LR_cell_comb: cell type pair combination
p.adj: adjusted p-value
PI: significance score: \(log2fc * -log10(p.adj)\)
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
res1 <- spatCellCellcom(
gobject = g,
cluster_column = "leiden_clus",
feat_set_1 = "Gm19935",
feat_set_2 = "9630013A20Rik",
verbose = "a lot",
random_iter = 10
)
#> Error in loadNamespace(x): there is no package called ‘future.apply’
force(res1)
#> Error: object 'res1' not found
res2 <- specificCellCellcommunicationScores(g,
cluster_column = "leiden_clus",
cell_type_1 = 1,
cell_type_2 = 2,
feat_set_1 = "Gm19935",
feat_set_2 = "9630013A20Rik"
)
#> simulations: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
force(res2)
#> Key: <LR_comb>
#> LR_comb lig_cell_type lig_expr ligand rec_cell_type rec_expr
#> <char> <fctr> <num> <char> <fctr> <num>
#> 1: Gm19935-9630013A20Rik 1 0 Gm19935 2 0
#> 2: Gm19935-9630013A20Rik 2 0 Gm19935 1 0
#> receptor LR_expr lig_nr rec_nr rand_expr av_diff log2fc pvalue
#> <char> <num> <int> <int> <num> <num> <num> <num>
#> 1: 9630013A20Rik 0 3 1 0.3771256 -0.3771256 -2.254369 0.78
#> 2: 9630013A20Rik 0 1 3 0.3717708 -0.3717708 -2.238086 0.79
#> LR_cell_comb p.adj PI
#> <char> <num> <num>
#> 1: 1--2 0.78 -0.2432586
#> 2: 2--1 0.79 -0.2291194