Visium spots are arranged in a hexagonal grid. This function uses the known locations of the Visium barcodes to construct a neighborhood graph, so adjacent spots are connected by edges. Since the known rows and columns of the spots are used, the unit the spot centroid coordinates are in does not matter.
Arguments
- x
A
SpatialFeatureExperimentobject with Visium data. Column names of the gene count matrix must be Visium barcodes, which may have a numeric suffix to distinguish between samples (e.g. "AAACAACGAATAGTTC-1").- sample_id
Which sample(s) in the SFE object to use for the graph. Can also be "all", which means this function will compute the graph for all samples independently.
- style
stylecan take values “W”, “B”, “C”, “U”, “minmax” and “S”- zero.policy
default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors
Value
For one sample, then a listw object representing the graph,
with an attribute "method" recording the function used to build the graph,
its arguments, and information about the geometry for which the graph was
built. The attribute is used to reconstruct the graphs when the SFE object
is subsetted since some nodes in the graph will no longer be present. If
sample_id = "all" or has length > 1, then a named list of listw
objects, whose names are the sample_ids. To add the list for multiple
samples to a SFE object, specify the name argument in the
spatialGraphs replacement method, so graph of the same name
will be added to the SFE object for each sample.
Examples
library(SFEData)
sfe <- McKellarMuscleData(dataset = "small")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
g <- findVisiumGraph(sfe)
# For multiple samples, returns named list
sfe2 <- McKellarMuscleData(dataset = "small2")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
sfe_combined <- cbind(sfe, sfe2)
gs <- findVisiumGraph(sfe, sample_id = "all")