filterICFSpot

Filter Interaction Changed Feature scores for spots.

filterICFSpot(
  icfObject,
  min_cells = 4,
  min_cells_expr_resi = 0.05,
  min_int_cells = 4,
  min_int_cells_expr_resi = 0.05,
  min_fdr = 0.5,
  min_pcc_diff = 0.05,
  min_zscore = 0.05,
  zscores_column = c("cell_type", "features"),
  direction = c("both", "up", "down")
)

Arguments

icfObject

ICF (interaction changed feature) score object

min_cells

minimum number of source cell type

min_cells_expr_resi

minimum expression residual level for source cell type

min_int_cells

minimum number of interacting neighbor cell type

min_int_cells_expr_resi

minimum expression residual level for interacting neighbor cell type

min_fdr

minimum adjusted p-value

min_pcc_diff

minimum absolute pcc difference difference

min_zscore

minimum z-score change

zscores_column

calculate z-scores over cell types or features

direction

differential expression directions to keep

Value

icfObject that contains the filtered differential feature scores

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
#> 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"
icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
#> Error: package 'future' is not yet installed
#> 
#>  To install:
#> install.packages(c("future"))

filterICFSpot(icfObject = icfObject)
#> Error: object 'icfObject' not found