spatialFastmap-methods {Cardinal} | R Documentation |
Performs spatially-aware FastMap projection.
## S4 method for signature 'SparseImagingExperiment' spatialFastmap(x, r = 1, ncomp = 3, method = c("gaussian", "adaptive"), dist = "chebyshev", tol.dist = 1e-9, iter.max = 1, BPPARAM = bpparam(), ...) ## S4 method for signature 'SpatialFastmap2' summary(object, ...) ## S4 method for signature 'SImageSet' spatialFastmap(x, r = 1, ncomp = 3, method = c("gaussian", "adaptive"), 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. |
dist |
The type of distance metric to use when calculating neighboring pixels based on |
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 |
An object of class SpatialFastmap2
, which is a ResultImagingExperiment
, 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
PCA
,
spatialKMeans
,
spatialShrunkenCentroids
register(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)