R: Run SpatialCPie

runCPie {SpatialCPie}

R Documentation

Run SpatialCPie

Description

Runs the SpatialCPie gadget.

Usage

runCPie(counts, image = NULL, spotCoordinates = NULL,
  margin = "spot", resolutions = 2:4,
  assignmentFunction = function(k, x) kmeans(x, centers = k)$cluster,
  view = NULL)

Arguments

`counts`	gene count matrix or a `SummarizedExperiment-class` object containing count values.
`image`	image to be used as background to the plot.
`spotCoordinates`	`data.frame` with pixel coordinates. The rows should correspond to the columns (spatial areas) in the count file.
`margin`	which margin to cluster.
`resolutions`	`numeric` vector specifying the clustering resolutions.
`assignmentFunction`	function to compute cluster assignments.
`view`	`viewer` object.

Value

a list with the following items:

"clusters": Cluster assignments (may differ from assignments)
"clusterGraph": The cluster tree ggplot object
"arrayPlot": The pie plot ggplot objects

Examples

if (interactive()) {
    options(device.ask.default = FALSE)

    ## Set up coordinate system
    coordinates <- as.matrix(expand.grid(1:10, 1:10))

    ## Generate data set with three distinct genes generated by three
    ## distinct cell types
    profiles <- diag(rep(1, 3)) + runif(9)
    centers <- cbind(c(5, 2), c(2, 8), c(8, 2))
    mixes <- apply(coordinates, 1, function(x) {
        x <- exp(-colSums((centers - x) ^ 2) / 50)
        x / sum(x)
    })
    means <- 100 * profiles %*% mixes
    counts <- matrix(rpois(prod(dim(means)), means), nrow = nrow(profiles))
    colnames(counts) <- apply(
        coordinates,
        1,
        function(x) do.call(paste, c(as.list(x), list(sep = "x")))
    )
    rownames(counts) <- paste("gene", 1:nrow(counts))

    ## Run SpatialCPie
    runCPie(counts)
}

[Package SpatialCPie version 1.3.4 Index]