spatialFastmap-methods: Spatially-aware FastMap projection
In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description Usage Arguments Value Author(s) References See Also Examples

Performs spatially-aware FastMap projection.

## S4 method for signature 'SparseImagingExperiment'
spatialFastmap(x, r = 1, ncomp = 3,
    method = c("gaussian", "adaptive"),
    metric = c("average", "correlation", "neighborhood"),
    dist = "chebyshev", tol.dist = 1e-9,
    iter.max = 1, BPPARAM = getCardinalBPPARAM(), ...)

## S4 method for signature 'SpatialFastmap2'
summary(object, ...)

## S4 method for signature 'SImageSet'
spatialFastmap(x, r = 1, ncomp = 3,
    method = c("gaussian", "adaptive"),
    metric = c("average", "correlation", "neighborhood"),
    iter.max = 1, ...)

`x`	The imaging dataset for which to calculate the FastMap components.
`r`	The neighborhood spatial smoothing radius.
`ncomp`	The number of FastMap components to calculate.
`method`	The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) weights, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) weights.
`metric`	The dissimilarity metric to use when comparing spectra, where ‘average’ (the default) means to use the differences of spatially-smoothed spectra, ‘correlation’ means to use the correlations of spatially-smoothed spectra, and ‘neighborhood’ means to use pairwise differences of each spectrum in the neighborhoods. Previous versions used ‘neighborhood’, which is the algorithm of Alexandrov & Kobarg; ‘average’ is the current default, because it handles non-gridded pixels better than ‘neighborhood’.
`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 number of iterations to perform when choosing the pivot vectors for each dimension.
`...`	Ignored.
`object`	A fitted model object to summarize.
`BPPARAM`	An optional instance of `BiocParallelParam`. See documentation for `bplapply`.

An object of class SpatialFastmap2, which is a ImagingResult, or an object of class SpatialFastmap, which is a ResultSet. Each element of the resultData slot contains at least the following components:

scores:: A matrix with the FastMap component scores.
correlation:: A matrix with the feature correlations with each FastMap component.
sdev:: The standard deviations of the FastMap scores.

Kylie A. Bemis

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.

PCA, spatialKMeans, spatialShrunkenCentroids

setCardinalBPPARAM(SerialParam())

set.seed(1)
data <- simulateImage(preset=2, npeaks=20, dim=c(6,6),
    representation="centroid")

# project to FastMap components
fm <- spatialFastmap(data, r=1, ncomp=2, method="adaptive")

# visualize first 2 components
image(fm, superpose=FALSE)