spatialKMeans-methods {Cardinal} | R Documentation

## Spatially-aware k-means clustering

### Description

Performs spatially-aware (SA) or spatially-aware structurally-adaptive (SASA) clustering of imaging data. The data are first projected into an embedded feature space where spatial structure is maintained using the Fastmap algorithm, and then ordinary k-means clustering is performed on the projected dataset.

### Usage

    ## S4 method for signature 'SparseImagingExperiment'
    spatialKMeans(x, r = 1, k = 3,
        method = c("gaussian", "adaptive"),
        dist = "chebyshev", tol.dist = 1e-9,
        iter.max = 10, nstart = 10,
        algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
        ncomp = 10, BPPARAM = bpparam(), ...)

    ## S4 method for signature 'SpatialKMeans2'
    summary(object, ...)

    ## S4 method for signature 'SImageSet'
    spatialKMeans(x, r = 1, k = 3,
        method = c("gaussian", "adaptive"),
        iter.max = 10, nstart = 10,
        algorithm = c("Hartigan-Wong", "Lloyd", "Forgy",
            "MacQueen"),
        ncomp = 10, ...)

### Arguments

`x` | The imaging dataset to cluster.
`r` | The spatial neighborhood radius of nearby pixels to consider. This can be a vector of multiple radii values.
`k` | The number of clusters. This can be a vector to try different numbers of clusters.
`method` | The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering.
`dist` | The type of distance metric to use when calculating neighboring pixels based on `r`. The options are ‘radial’, ‘manhattan’, ‘minkowski’, and ‘chebyshev’ (the default).
`tol.dist` | The distance tolerance used for matching pixels when calculating pairwise distances between neighborhoods. This parameter should only matter when the data is not gridded. (Only considers ‘radial’ distance.)
`iter.max` | The maximum number of k-means iterations.
`nstart` | The number of restarts for the k-means algorithm.
`algorithm` | The k-means algorithm to use. See `kmeans` for details.
`ncomp` | The number of fastmap components to calculate.
`...` | Ignored.
`object` | A fitted model object to summarize.
`BPPARAM` | An optional instance of `BiocParallelParam`. See documentation for `bplapply`.

### Value

An object of class `SpatialKMeans2`, which is a `ImagingResult`, or an object of class `SpatialKMeans`, which is a `ResultSet`. Each element of the `resultData` slot contains at least the following components:

`cluster`:

  A vector of integers indicating the cluster for each pixel in the dataset.

`centers`:

  A matrix of cluster centers.

`correlation`:

  A matrix with the feature correlations with each cluster.

### Author(s)

Kylie A. Bemis

### References

Alexandrov, T., & Kobarg, J. H. (2011). Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics, 27(13), i230-i238. doi:10.1093/bioinformatics/btr246

Faloutsos, C., & Lin, D. (1995). FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. Presented at the Proceedings of the 1995 ACM SIGMOD international conference on Management of data.

### See Also

`spatialShrunkenCentroids`

### Examples

    register(SerialParam())

    set.seed(1)
    x <- simulateImage(preset=3, dim=c(10,10), npeaks=10,
        peakheight=c(4,6,8), representation="centroid")

    res <- spatialKMeans(x, r=1, k=4, method="adaptive")

    summary(res)

    image(res, model=1)

[Package Cardinal version 2.4.0 Index]