calcNhoodDistance: Calculate within neighbourhood distances

In MarioniLab/miloR: Differential neighbourhood abundance testing on a graph

Calculate within neighbourhood distances

Description

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.

Usage

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.

Author(s)

Mike Morgan, Emma Dann

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)
milo <- makeNhoods(milo)
milo <- calcNhoodDistance(milo, d=30)

milo

MarioniLab/miloR documentation built on Oct. 18, 2024, 6:04 p.m.