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 |
returns a spatial correlation object: "spatCorObject"
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()