| detectSpatialCorGenes | R Documentation | 
Detect genes that are spatially correlated
detectSpatialCorGenes(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  method = c("grid", "network"),
  expression_values = c("normalized", "scaled", "custom"),
  subset_feats = NULL,
  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 | 
| feat_type | feature type | 
| spat_unit | spatial unit | 
| method | method to use for spatial averaging | 
| expression_values | gene expression values to use | 
| subset_feats | subset of feats to use | 
| subset_genes | deprecated, 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
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