Detect Genes Using Spatial Correlation¶

detectSpatialCorGenes()

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	method to use for spatial averaging
expression_values	gene expression values to use
subset_genes	subset of genes to use
spatial_network_name	name of spatial network to use
network_smoothing	smoothing factor beteen 0 and 1 (default: automatic)
spatial_grid_name	name of spatial grid to use
min_cells_per_grid	minimum number of cells to consider a grid
cor_method	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.

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