runCPie: Run SpatialCPie

In SpatialCPie: Cluster analysis of Spatial Transcriptomics data

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)
}

SpatialCPie documentation built on Nov. 8, 2020, 7:43 p.m.