Find spatial neighborhood graph

This function wraps all spatial neighborhood graphs implemented in the package spdep for the SpatialFeatureExperiment (SFE) class, to find spatial neighborhood graphs for the entities represented by columns or rows of the gene count matrix in the SFE object or spatial entities in the annotGeometries field of the SFE object. Results are stored as listw objects in the spatialGraphs field of the SFE object, as listw is used in many methods that facilitate the spatial neighborhood graph in the spdep, spatialreg, and adespatial. The edge weights of the graph in the listw object are by default style W (see nb2listw) and the unweighted neighbor list is in the neighbours field of the listw object.

Usage

# S4 method for class 'SpatialFeatureExperiment'
findSpatialNeighbors(
  x,
  sample_id = "all",
  type = "spatialCoords",
  MARGIN = 2,
  method = c("tri2nb", "knearneigh", "dnearneigh", "gabrielneigh", "relativeneigh",
    "soi.graph", "poly2nb"),
  dist_type = c("none", "idw", "exp", "dpd"),
  glist = NULL,
  style = c("raw", "W", "B", "C", "U", "minmax", "S"),
  nn_method = c("bioc", "spdep"),
  alpha = 1,
  dmax = NULL,
  BPPARAM = SerialParam(),
  BNPARAM = KmknnParam(),
  zero.policy = TRUE,
  ...
)

Arguments

x

A SpatialFeatureExperiment object.

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.

type

Name of the geometry associated with the MARGIN of interest for which to compute the graph.

MARGIN

Just like in apply, where 1 stands for row, 2 stands for column. Here, in addition, 3 stands for annotation, to query the annotGeometries, such as nuclei segmentation in a Visium data

method

Name of function in the package spdep to use to find the spatial neighborhood graph.

dist_type

Type of distance-based weight. "none" means not using distance-based weights; the edge weights of the spatial neighborhood graph will be entirely determined by the style argument. "idw" means inverse distance weighting. "exp" means exponential decay. "dpd" means double-power distance weights. See nb2listwdist for details.

glist

list of general weights corresponding to neighbours

style

style can take values “W”, “B”, “C”, “U”, “minmax” and “S”

nn_method

Method to find k nearest neighbors and distance based neighbors. Can be either "bioc" or "spdep". For "bioc", methods from BiocNeighbors are used. For "spdep", methods from the spdep package are used. The "bioc" option is more scalable to larger datasets and supports multithreading.

alpha

Only relevant when dist_type = "dpd".

dmax

Only relevant when dist_type = "dpd".

BPPARAM

A BiocParallelParam object for multithreading. Only used for k nearest neighbor and distance based neighbor with nn_method = "bioc".

BNPARAM

A BiocNeighborParam object specifying the algorithm to find k nearest neighbors and distance based neighbors with nn_method = "bioc". For distance based neighbors, only KmknnParam and VptreeParam are applicable.

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

...

Extra arguments passed to the spdep function stated in the method argument, such as k, use_kd_tree, d1, d2, nnmult, sym, and quadsegs. Note that any arguments about using longitude and latitude, which are irrelevant, are ignored.

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.

Note

style = "raw" is only applicable when dist_type is not "none". If dist_type = "none" and style = "raw", then style will default to "W". Using distance based weights does not supplant finding a spatial neighborhood graph. The spatial neighborhood graph is first found and then its edges weighted based on distance in this function.

Examples

library(SFEData)
sfe <- McKellarMuscleData(dataset = "small")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
# sample_id is optional when only one sample is present
g <- findSpatialNeighbors(sfe, sample_id = "Vis5A")
attr(g, "method")
#> $FUN
#> [1] "findSpatialNeighbors"
#> 
#> $package
#> $package[[1]]
#> [1] "SpatialFeatureExperiment"
#> 
#> $package[[2]]
#> [1] ‘1.7.1’
#> 
#> 
#> $args
#> $args$method
#> [1] "tri2nb"
#> 
#> $args$row.names
#>  [1] "AAATTACCTATCGATG" "AACATATCAACTGGTG" "AAGATTGGCGGAACGT" "AAGGGACAGATTCTGT"
#>  [5] "AATATCGAGGGTTCTC" "AATGATGATACGCTAT" "AATGATGCGACTCCTG" "AATTCATAAGGGATCT"
#>  [9] "ACGAGTACGGATGCCC" "ACGCTAGTGATACACT" "AGATCGTGCATAAGAT" "AGCGCGGGTGCCAATG"
#> [13] "AGCTAGAAGCAGAAGT" "AGGTCGCGGAGTTACT" "AGTGAGCCTCGCCGCC" "AGTGGCGGCAATTTGA"
#> [17] "ATAAGTAGGATTCAGA" "ATAGACAACGGGACCT" "ATAGAGTTATCAACTT" "ATAGGCTAGCTTCGCA"
#> [21] "ATCTGCACCTCTGCGA" "ATTACTTACTGGGCAT" "CAGCTCGACAAGTTAA" "CAGTAGCCCACGCGGT"
#> [25] "CAGTCGAGGATGCAAT" "CATGCGACCAGTTTAA" "CCAGTAGTCTGATCCA" "CCATACCTTTACTTGT"
#> [29] "CCTAACTAAGGCTCTA" "CCTCACCTGAGGGAGC" "CCTCACCTTAGCATCG" "CCTCGCGCGATATAGG"
#> [33] "CCTTTCAATGAAGAAA" "CGACTCAGGATGTTAT" "CGATGGACCCTACGCC" "CGGTAGAGGTGCAGGT"
#> [37] "CGGTTATCCAACAGTG" "CGGTTCAAGTAGGTGT" "CTAACGAAACTTGCTG" "CTACTATCTTTCAGAG"
#> [41] "CTAGCCGATGTTATGA" "CTATAAGAGCCAATCG" "CTCAGTCACGACAAAT" "CTCGATATTTGCGAGC"
#> [45] "CTCGTTTCTAATGTTT" "CTGGTAACGAGCTCTT" "CTTGTGCTCACCGATT" "GACGGTCAATAGAAGC"
#> [49] "GACTCGCGGGAATGAC" "GAGGGCGCAGCTCTGC" "GATCCTAAATCGGGAC" "GCCAATAGGGCATCTC"
#> [53] "GCCATATTGCACACAG" "GCCTATAGTGTCAGGG" "GCGCATCCAGTCAGCA" "GCGCTGCTTTGCATTT"
#> [57] "GGGCTCATCGAACCCA" "GGTCGGATAAACGGCG" "GTACTCCTGGGTATGC" "GTGAACTCCCATTCGA"
#> [61] "GTGAAGATTTCAAGTG" "TAATCGATCCGTACGT" "TACTGCAATCAATTAC" "TACTTTACTGAGCCGG"
#> [65] "TAGTCTGTGACGTTGC" "TATCACCCAACCGACC" "TCCGGCCTAGCGTACA" "TCCGTTTAGCCTTGAA"
#> [69] "TCTAGGTGGCGACGCT" "TCTTACCGGAACTCGT" "TGGAACCACTGACACA" "TTACAGACCTAAATGA"
#> [73] "TTAGCTAATACGATCT" "TTCCAATCAGAGCTAG" "TTCCGCAGAGAAATAT" "TTCTTTGGTCGCGACG"
#> [77] "TTGATGTGTAGTCCCG"
#> 
#> $args$dist_type
#> [1] "none"
#> 
#> $args$style
#> [1] "W"
#> 
#> $args$alpha
#> [1] 1
#> 
#> $args$zero.policy
#> [1] TRUE
#> 
#> $args$sample_id
#> [1] "Vis5A"
#> 
#> $args$type
#> [1] "spatialCoords"
#> 
#> $args$MARGIN
#> [1] 2
#> 
#> 
# Returns named list for multiple samples
sfe2 <- McKellarMuscleData(dataset = "small2")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
sfe_combined <- cbind(sfe, sfe2)
gs <- findSpatialNeighbors(sfe, sample_id = "all")