countCells {miloR} | R Documentation

## Count cells in neighbourhoods

### Description

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.

### Usage

    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.

### 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.

### Value

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

### Author(s)

Mike Morgan, Emma Dann

### Examples

    library(igraph)
    m <- matrix(rnorm(100000), ncol=100)
    milo <- buildGraph(t(m), k=20, d=10)
    milo <- makeNhoods(milo, k=20, d=10, prop=0.3)

    cond <- rep("A", nrow(m))
    cond.a <- sample(seq_len(nrow(m)), size=floor(nrow(m)*0.25))
    cond.b <- setdiff(seq_len(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")
    milo

[Package miloR version 1.2.0 Index]