detectSpatialCorFeatsMatrix#
Last Updated: Sep 07, 2023
Description#
Detect genes that are spatially correlated
Usage#
detectSpatialCorFeatsMatrix(
expression_matrix,
method = c("grid", "network"),
spatial_network,
spatial_grid,
spatial_locs,
subset_feats = NULL,
network_smoothing = NULL,
min_cells_per_grid = 4,
cor_method = c("pearson", "kendall", "spearman")
)
Arguments#
|
provided expression matrix |
|
method to use for spatial averaging |
|
provided spatial network |
|
provided spatial grid |
|
provided spatial locations |
|
subset of features to use |
|
smoothing factor beteen 0 and 1 (default: automatic) |
|
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
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()
Value#
returns a spatial correlation object: “spatCorObject”
See Also#
showSpatialCorFeats