runPAGEEnrich

Function to calculate gene signature enrichment scores per spatial position using PAGE.

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

gobject

Giotto object

spat_unit

spatial unit

feat_type

feature type

sign_matrix

Matrix of signature genes for each cell type / process

expression_values

expression values to use

min_overlap_genes

minimum number of overlapping genes 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

calculate p-values (boolean, default = FALSE)

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

data.table with enrichment results

Details

sign_matrix: a binary matrix with genes as row names and cell-types as column names. Alternatively a list of signature genes can be provided to makeSignMatrixPAGE, which will create the matrix for you.

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.

See also

makeSignMatrixPAGE

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
#> 
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"
sign_gene <- c(
    "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
    "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
    "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
    "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
)

sign_matrix <- matrix(rnorm(length(sign_gene) * 3, mean = 10),
    nrow = length(sign_gene)
)
rownames(sign_matrix) <- sign_gene
colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")

runPAGEEnrich(gobject = g, sign_matrix = sign_matrix)
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS PAGE
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used