image-methods {Cardinal} | R Documentation |
Create and display images for the pixel data of an imaging dataset using a formula interface.
## S4 method for signature 'formula' image(x, data = environment(x), ..., xlab, ylab, zlab, subset) #### Methods for Cardinal >= 2.x classes #### ## S4 method for signature 'PositionDataFrame' image(x, formula, groups = NULL, superpose = FALSE, strip = TRUE, key = superpose || !is.null(groups), normalize.image = c("none", "linear"), contrast.enhance = c("none", "suppression", "histogram"), smooth.image = c("none", "gaussian", "adaptive"), ..., xlab, xlim, ylab, ylim, zlab, zlim, asp = 1, layout, col = discrete.colors, colorscale = viridis, colorkey = !key, alpha.power = 1, subset = TRUE, add = FALSE) ## S4 method for signature 'SparseImagingExperiment' image(x, formula, feature, feature.groups, groups = NULL, superpose = FALSE, strip = TRUE, key = superpose || !is.null(groups), fun = mean, normalize.image = c("none", "linear"), contrast.enhance = c("none", "suppression", "histogram"), smooth.image = c("none", "gaussian", "adaptive"), ..., xlab, xlim, ylab, ylim, zlab, zlim, asp = 1, layout, col = discrete.colors, colorscale = viridis, colorkey = !key, alpha.power = 1, subset = TRUE, add = FALSE) ## S4 method for signature 'SparseImagingExperiment' image3D(x, formula, ..., alpha.power = 2) ## S4 method for signature 'MSImagingExperiment' image(x, formula, feature = features(x, mz=mz), feature.groups, mz, plusminus, ...) ## S4 method for signature 'SparseResultImagingExperiment' image(x, formula, model = modelData(x), superpose = TRUE, ..., column, colorscale = cividis, colorkey = !superpose, alpha.power = 2, subset = TRUE) ## S4 method for signature 'PCA2' image(x, formula, values = "scores", ...) ## S4 method for signature 'PLS2' image(x, formula, values = c("fitted", "scores"), ...) ## S4 method for signature 'SpatialFastmap2' image(x, formula, values = "scores", ...) ## S4 method for signature 'SpatialKMeans2' image(x, formula, values = "cluster", ...) ## S4 method for signature 'SpatialShrunkenCentroids2' image(x, formula, values = c("probability", "class", "scores"), ...) ## S4 method for signature 'SpatialDGMM' image(x, formula, values = c("probability", "class"), ...) ## S4 method for signature 'SegmentationTest' image(x, formula, values = "mapping", ...) #### Methods for Cardinal 1.x classes #### ## S4 method for signature 'SImageSet' image(x, formula = ~ x * y, feature, feature.groups, groups = NULL, superpose = FALSE, strip = TRUE, key = superpose, fun = mean, normalize.image = c("none", "linear"), contrast.enhance = c("none", "suppression", "histogram"), smooth.image = c("none", "gaussian", "adaptive"), ..., xlab, xlim, ylab, ylim, zlab, zlim, layout, asp = 1, col = rainbow(nlevels(groups)), col.regions = intensity.colors(100), colorkey = !is3d, subset = TRUE, lattice = FALSE) ## S4 method for signature 'SImageSet' image3D(x, formula = ~ x * y * z, ...) ## S4 method for signature 'MSImageSet' image(x, formula = ~ x * y, feature = features(x, mz=mz), feature.groups, mz, plusminus, ...) ## S4 method for signature 'ResultSet' image(x, formula, model = pData(modelData(x)), feature, feature.groups, superpose = TRUE, strip = TRUE, key = superpose, ..., column, col = if (superpose) rainbow(nlevels(feature.groups)) else "black", lattice = FALSE) ## S4 method for signature 'CrossValidated' image(x, fold = 1:length(x), layout, ...) ## S4 method for signature 'PCA' image(x, formula = substitute(mode ~ x * y), mode = "scores", ...) ## S4 method for signature 'PLS' image(x, formula = substitute(mode ~ x * y), mode = c("fitted", "scores", "y"), ...) ## S4 method for signature 'OPLS' image(x, formula = substitute(mode ~ x * y), mode = c("fitted", "scores", "Oscores", "y"), ...) ## S4 method for signature 'SpatialFastmap' image(x, formula = substitute(mode ~ x * y), mode = "scores", ...) ## S4 method for signature 'SpatialShrunkenCentroids' image(x, formula = substitute(mode ~ x * y), mode = c("probabilities", "classes", "scores"), ...) ## S4 method for signature 'SpatialKMeans' image(x, formula = substitute(mode ~ x * y), mode = "cluster", ...)
x |
An imaging dataset. |
formula |
A formula of the form 'z ~ x * y | g1 * g2 * ...' (or equivalently, 'z ~ x + y | g1 + g2 + ...'), indicating a LHS 'y' (on the y-axis) versus a RHS 'x' (on the x-axis) and conditioning variables 'g1, g2, ...'. Usually, the LHS is not supplied, and the formula is of the form '~ x * y | g1 * g2 * ...', and the y-axis is implicityl assumed to be the feature vectors corresponding to each pixel in the imaging dataset specified by the object 'x'. However, a variable evaluating to a vector of pixel values, or a sequence of such variables, can also be supplied. The RHS is evaluated in The conditioning variables are evaluated in |
data |
A |
mz |
The m/z value(s) for which to plot the ion image(s). |
plusminus |
If specified, a window of m/z values surrounding the one given by |
feature |
The feature or vector of features for which to plot the image. This is an expression that evaluates to a logical or integer indexing vector. |
feature.groups |
An alternative way to express a single conditioning variable. This is a variable or expression to be evaluated in |
groups |
A variable or expression to be evaluated in |
superpose |
Should feature vectors from different feature groups specified by 'feature.groups' be superposed on the same plot? If 'TRUE' then the 'groups' argument is ignored. |
strip |
Should strip labels indicating the plotting group be plotting along with the each panel? Passed to 'strip' in |
key |
A logical, or |
fun |
A function to apply over pixel vectors of images grouped together by 'feature.groups'. By default, this is used for averaging over features. |
normalize.image |
Normalization function to be applied to each image. The function can be user-supplied, of one of 'none' or 'linear'. The 'linear' normalization method normalized each image to the same intensity range using a linear transformation. |
contrast.enhance |
Contrast enhancement function to be applied to each image. The function can be user-supplied, or one of 'none', 'histogram', or 'suppression'. The 'histogram' equalization method flatterns the distribution of intensities. The hotspot 'suppression' method uses thresholding to reduce the intensities of hotspots. |
smooth.image |
Image smoothing function to be applied to each image. The function can be user-supplied, or one of 'none', 'gaussian', or 'adaptive'. The 'gaussian' smoothing method smooths images with a simple gaussian kernel. The 'adaptive' method uses bilateral filtering to preserve edges. |
xlab |
Character or expression giving the label for the x-axis. |
ylab |
Character or expression giving the label for the y-axis. |
zlab |
Character or expression giving the label for the z-axis. (Only used for plotting 3D images.) |
xlim |
A numeric vector of length 2 giving the left and right limits for the x-axis. |
ylim |
A numeric vector of length 2 giving the top and bottom limits for the y-axis. |
zlim |
A numeric vector of length 2 giving the lower and upper limits for the z-axis (i.e., the range of colors to be plotted). |
layout |
The layout of the plots, given by a length 2 numeric as |
asp |
The aspect ratio of the plot. |
col |
A specification for the default plotting color(s) for groups. |
colorscale |
The color scale to use for the z-axis of image intensities. This may be either a vector of colors or a function which takes a single numeric argument |
col.regions |
The default plotting color(s) for the z-axis of image intensities. Thus must be a vector of colors. |
colorkey |
Should a coloykey describing the z-axis be drawn with the plot? |
alpha.power |
Opacity scaling factor (1 is linear). |
subset |
An expression that evaluates to a logical or integer indexing vector to be evaluated in |
... |
additional arguments passed to the underlying |
fold |
What folds of the cross-validation should be plotted. |
model |
A vector or |
mode |
What kind of results should be plotted. This is the name of the object to plot in the |
values |
What kind of results should be plotted. This is the name of the object to plot in the |
column |
What columns of the results should be plotted. If the results are a matrix, this corresponds to the columns to be plotted, which can be indicated either by numeric index or by name. |
lattice |
Should lattice graphics be used to create the plot? |
add |
Should the method call |
For objects derived from class SImageSet, calling image3D(x)
is equivalent to image(x, ~ x * y * z)
.
Kylie A. Bemis
register(SerialParam()) set.seed(1) x <- simulateImage(preset=2, npeaks=10, dim=c(10,10)) m <- mz(metadata(x)$design$featureData) image(x, mz=m[1], plusminus=0.5) image(x, mz=m[1], smooth.image="gaussian", contrast.enhance="histogram") image(x, mz=m[1], colorscale=col.map("grayscale")) image(x, mz=m[4:7], colorscale=col.map("cividis")) image(x, mz=m[c(1,8)], normalize.image="linear", superpose=TRUE) pixelData(x)$tic <- summarize(x, .by="pixel", .stat=c(tic="sum"))$tic image(x, tic ~ x * y, colorscale=magma)