detectSpatialCorGenes
¶
detectSpatialCorGenes
Description¶
Detect genes that are spatially correlated
Usage¶
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")
)
Arguments¶
Argument |
Description |
---|---|
|
giotto object |
|
feature type |
|
spatial unit |
|
method to use for spatial averaging |
|
gene expression values to use |
|
subset of feats to use |
|
deprecated, 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 |
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` <#createspatialnetwork>`_ function.
list(“1. grid-averaging: “) list(“average gene expression values within a predefined spatial grid”)
list(“2. network-averaging: “) list(“smoothens the gene expression matrix by averaging the expression within one celln”, ” by using the neighbours within the predefined spatial network. b is a smoothening factorn”, ” that defaults to 1 - 1/k, where k is the median number of k-neighbors in then”, ” selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contributionn”, ” from its own expression.”)
The spatCorObject can be further explored with showSpatialCorGenes()
Value¶
returns a spatial correlation object: “spatCorObject”
Seealso¶
``showSpatialCorGenes` <#showspatialcorgenes>`_