Calculate within neighbourhood distances

This function will calculate Euclidean distances between single-cells in a neighbourhood using the same dimensionality as was used to construct the graph. This step follows the makeNhoods call to limit the number of distance calculations required.

calcNhoodDistance(x, d, reduced.dim = NULL, use.assay = "logcounts")

Arguments

x	A Milo object with a valid graph slot. If reduced.dims is not provided and there is no valid populated reducedDim slot in x, then this is computed first with d + 1 principal components.
d	The number of dimensions to use for computing within-neighbourhood distances. This should be the same value used construct the graph.
reduced.dim	If x is an Milo object, a character indicating the name of the reducedDim slot in the Milo object to use as (default: 'PCA'). Otherwise this should be an N X P matrix with rows in the same order as the columns of the input Milo object x.
use.assay	A character scalar defining which assay slot in the Milo to use

Value

A Milo object with the distance slots populated.

Examples

library(SingleCellExperiment)
ux <- matrix(rpois(12000, 5), ncol=200)
vx <- log2(ux + 1)
pca <- prcomp(t(vx))

sce <- SingleCellExperiment(assays=list(counts=ux, logcounts=vx),
                            reducedDims=SimpleList(PCA=pca$x))

milo <- Milo(sce)
milo <- buildGraph(milo, d=30, transposed=TRUE)
#> Constructing kNN graph with k:10
milo <- calcNhoodDistance(milo, d=30)

milo
#> class: Milo 
#> dim: 60 200 
#> metadata(0):
#> assays(2): counts logcounts
#> rownames: NULL
#> rowData names(0):
#> colnames: NULL
#> colData names(0):
#> reducedDimNames(1): PCA
#> spikeNames(0):
#> altExpNames(0):
#> nhoods dimensions(1): 0
#> nhoodCounts dimensions(2): 1 1
#> nhoodDistances dimension(1): 0
#> graph names(1): graph
#> nhoodIndex names(1): 0
#> nhoodExpression dimension(2): 1 1
#> nhoodReducedDim names(0):
#> nhoodGraph names(0):
#> nhoodAdjacency dimension(0):