Count cells in neighbourhoods

This function quantifies the number of cells in each neighbourhood according to an input experimental design. This forms the basis for the differential neighbourhood abundance testing.

countCells(x, samples, meta.data = NULL)

Arguments

x	A Milo object with non-empty graph and nhoods slots.
samples	Either a string specifying which column of data should be used to identify the experimental samples for counting, or a named vector of sample ids mapping each single cell to it's respective sample.
meta.data	A cell X variable data.frame containing study meta-data including experimental sample IDs. Assumed to be in the same order as the cells in the input Milo object.

Value

A Milo object containing a counts matrix in the nhoodCounts slot.

Details

This function generates a counts matrix of nhoods X samples, and populates the nhoodCounts slot of the input Milo object. This matrix is used down-stream for differential abundance testing.

Examples

library(igraph)
#> 
#> Attaching package: ‘igraph’
#> The following objects are masked from ‘package:DelayedArray’:
#> 
#>     path, simplify
#> The following object is masked from ‘package:GenomicRanges’:
#> 
#>     union
#> The following object is masked from ‘package:IRanges’:
#> 
#>     union
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     union
#> The following objects are masked from ‘package:BiocGenerics’:
#> 
#>     normalize, path, union
#> The following object is masked from ‘package:miloR’:
#> 
#>     graph
#> The following objects are masked from ‘package:stats’:
#> 
#>     decompose, spectrum
#> The following object is masked from ‘package:base’:
#> 
#>     union
m <- matrix(rnorm(100000), ncol=100)
milo <- buildGraph(m, k=20, d=10)
#> Constructing kNN graph with k:20
milo <- makeNhoods(milo, k=20, d=10, prop=0.3)
#> Checking valid object
#> Warning: Rownames not set on reducedDims - setting to row indices

cond <- rep("A", nrow(m))
cond.a <- sample(1:nrow(m), size=floor(nrow(m)*0.25))
cond.b <- setdiff(1:nrow(m), cond.a)
cond[cond.b] <- "B"
meta.df <- data.frame(Condition=cond, Replicate=c(rep("R1", 330), rep("R2", 330), rep("R3", 340)))
meta.df$SampID <- paste(meta.df$Condition, meta.df$Replicate, sep="_")
milo <- countCells(milo, meta.data=meta.df, samples="SampID")
#> Checking meta.data validity
#> Setting up matrix with 12 neighbourhoods
#> Counting cells in neighbourhoods
milo
#> class: Milo 
#> dim: 1000 100 
#> metadata(0):
#> assays(1): logcounts
#> rownames: NULL
#> rowData names(0):
#> colnames: NULL
#> colData names(0):
#> reducedDimNames(1): PCA
#> spikeNames(0):
#> altExpNames(0):
#> nhoods dimensions(1): 12
#> nhoodCounts dimensions(2): 12 6
#> nhoodDistances dimension(1): 0
#> graph names(1): graph
#> nhoodIndex names(1): 12
#> nhoodExpression dimension(2): 1 1
#> nhoodReducedDim names(0):
#> nhoodGraph names(0):
#> nhoodAdjacency dimension(0):