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 1.4.0 Index]