Detect genes that are spatially correlated

detectSpatialCorGenes(
gobject,
method = c("grid", "network"),
expression_values = c("normalized", "scaled", "custom"),
subset_genes = NULL,
spatial_network_name = "Delaunay_network",
network_smoothing = NULL,
spatial_grid_name = "spatial_grid",
min_cells_per_grid = 4,
cor_method = c("pearson", "kendall", "spearman")
)

## Arguments

gobject giotto object method to use for spatial averaging gene expression values to use subset of genes to use name of spatial network to use smoothing factor beteen 0 and 1 (default: automatic) name of spatial grid to use minimum number of cells to consider a grid correlation method

## Value

returns a spatial correlation object: "spatCorObject"

## Details

For method = network, it expects a fully connected spatial network. You can make sure to create a fully connected network by setting minimal_k > 0 in the createSpatialNetwork function.

• 1. grid-averaging: average gene expression values within a predefined spatial grid

• 2. network-averaging: smoothens the gene expression matrix by averaging the expression within one cell by using the neighbours within the predefined spatial network. b is a smoothening factor that defaults to 1 - 1/k, where k is the median number of k-neighbors in the selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contribution from its own expression.

The spatCorObject can be further explored with showSpatialCorGenes()

showSpatialCorGenes