PAGE feature enrichment — enrichment

Expression feature-based enrichment scoring of labels.
A binary matrix of signature features (e.g. for cell types or processes) can either be directly provided or converted from a list using makeSignMatrixPAGE(). This matrix is then used with runPAGEEnrich() in order to calculate feature signature enrichment scores per spatial position using PAGE.

makeSignMatrixPAGE(sign_names, sign_list)

runPAGEEnrich(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  sign_matrix,
  expression_values = c("normalized", "scaled", "custom"),
  min_overlap_genes = 5,
  reverse_log_scale = TRUE,
  logbase = 2,
  output_enrichment = c("original", "zscore"),
  p_value = FALSE,
  include_depletion = FALSE,
  n_times = 1000,
  max_block = 2e+07,
  name = NULL,
  verbose = TRUE,
  return_gobject = TRUE
)

Arguments

sign_names: character vector with names (labels) for each provided feat signature
sign_list: list of feats in signature
gobject: Giotto object
spat_unit: spatial unit
feat_type: feature type
sign_matrix: binary matrix of signature feats for each cell type / process. Alternatively a list of signature feats can be provided to makeSignMatrixPAGE(), which will create the matrix for you.
expression_values: expression values to use
min_overlap_genes: minimum number of overlapping feats in sign_matrix required to calculate enrichment
reverse_log_scale: reverse expression values from log scale
logbase: log base to use if reverse_log_scale = TRUE
output_enrichment: how to return enrichment output
p_value: logical. Default = FALSE. calculate p-values
include_depletion: calculate both enrichment and depletion
n_times: number of permutations to calculate for p_value
max_block: number of lines to process together (default = 20e6)
name: to give to spatial enrichment results, default = PAGE
verbose: be verbose
return_gobject: return giotto object

Value

matrix (makeSignMatrixPAGE()) and giotto (runPAGEEnrich(return_gobject = TRUE)) or data.table (runPAGEEnrich(return_gobject = FALSE))

Details

The enrichment Z score is calculated by using method (PAGE) from Kim SY et al., BMC bioinformatics, 2005 as
\(Z = ((Sm – mu)*m^(1/2)) / delta\).
For each gene in each spot, mu is the fold change values versus the mean expression and delta is the standard deviation. Sm is the mean fold change value of a specific marker gene set and m is the size of a given marker gene set.

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 : 3.12

sign_list <- list(
    cell_type1 = c(
        "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c",
        "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd"
    ),
    cell_type2 = c(
        "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3",
        "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a"
    ),
    cell_type2 = c(
        "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2",
        "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
    )
)

sm <- makeSignMatrixPAGE(
    sign_names = c("cell_type1", "cell_type2", "cell_type3"),
    sign_list = sign_list
)

g <- runPAGEEnrich(
    gobject = g,
    sign_matrix = sm,
    min_overlap_genes = 2
)
#> Setting spatial enrichment [cell][rna] PAGE

spatPlot2D(g,
    cell_color = "cell_type2",
    spat_enr_names = "PAGE",
    color_as_factor = FALSE
)