findInteractionChangedFeats

Identifies cell-to-cell Interaction Changed Features (ICF), i.e. features that are differentially expressed due to proximity to other cell types.

findInteractionChangedFeats(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = "normalized",
  selected_feats = NULL,
  cluster_column,
  spatial_network_name = "Delaunay_network",
  minimum_unique_cells = 1,
  minimum_unique_int_cells = 1,
  diff_test = c("permutation", "limma", "t.test", "wilcox"),
  mean_method = c("arithmic", "geometric"),
  offset = 0.1,
  adjust_method = c("bonferroni", "BH", "holm", "hochberg", "hommel", "BY", "fdr",
    "none"),
  nr_permutations = 1000,
  exclude_selected_cells_from_test = TRUE,
  do_parallel = TRUE,
  set_seed = TRUE,
  seed_number = 1234
)

Arguments

gobject

giotto object

feat_type

feature type

spat_unit

spatial unit

expression_values

expression values to use

selected_feats

subset of selected features (optional)

cluster_column

name of column to use for cell types

spatial_network_name

name of spatial network to use

minimum_unique_cells

minimum number of target cells required

minimum_unique_int_cells

minimum number of interacting cells required

diff_test

which differential expression test

mean_method

method to use to calculate the mean

offset

offset value to use when calculating log2 ratio

adjust_method

which method to adjust p-values

nr_permutations

number of permutations if diff_test = permutation

exclude_selected_cells_from_test

exclude interacting cells other cells

do_parallel

run calculations in parallel with mclapply

set_seed

set a seed for reproducibility

seed_number

seed number

Value

icfObject that contains the Interaction Changed differential feature scores

Details

Function to calculate if features are differentially expressed in cell types when they interact (approximated by physical proximity) with other cell types. The results data.table in the icfObject contains - at least - the following columns:

• features: All or selected list of tested features

• sel: average feature expression in the interacting cells from the target cell type

• other: average feature expression in the NOT-interacting cells from the target cell type

• log2fc: log2 fold-change between sel and other

• diff: spatial expression difference between sel and other

• p.value: associated p-value

• p.adj: adjusted p-value

• cell_type: target cell type

• int_cell_type: interacting cell type

• nr_select: number of cells for selected target cell type

• int_nr_select: number of cells for interacting cell type

• nr_other: number of other cells of selected target cell type

• int_nr_other: number of other cells for interacting cell type

• unif_int: cell-cell interaction

Examples

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
#> cell_spatInfo_spatVector.shp
#> cell
#> 
#> 3.2 read Giotto spatial centroid information
#> cell
#> 
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#>  skipped
#> 
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#>  "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"

findInteractionChangedFeats(g, cluster_column = "leiden_clus",
selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
#> An object of class icfObject 
#>  -dimensions : 117, 20 (icfs, attributes)
#> <giotto info>
#>  -values          : normalized
#>  -cluster         : leiden_clus
#>  -spatial network : Delaunay_network
#> <test info>
#>  -test                   : permutation
#>  -p.adj                  : bonferroni
#>  -min cells              : 1
#>  -min interacting cells  : 1
#>  -exclude selected cells : TRUE
#>  -perm                   : 10