detectSpatialCorGenes: detectSpatialCorGenes
In bernard2012/Giotto: Spatial single-cell transcriptomics toolbox

Description Usage Arguments Details Value See Also Examples

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")
)

`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

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()

returns a spatial correlation object: "spatCorObject"

showSpatialCorGenes