Highlight features of interest on a spatial proteomics plot

Highlights a set of features of interest given as a FeaturesOfInterest instance on a PCA plot produced by plot2D or plot3D. If none of the features of interest are found in the MSnset's featureNames, an warning is thrown.

Usage

highlightOnPlot(object, foi, labels, args = list(), ...)

highlightOnPlot3D(object, foi, labels, args = list(), radius = 0.1 * 3, ...)

Arguments

object: The main dataset described as an MSnSet or a matrix with the coordinates of the features on the PCA plot produced (and invisibly returned) by plot2D.
foi: An instance of FeaturesOfInterest, or, alternatively, a character of feautre names.
labels: A character of length 1 with a feature variable name to be used to label the features of interest. This is only valid if object is an MSnSet. Alternatively, if TRUE, then featureNames(object) (or rownames(object), if object is a matrix) are used. Default is missing, which does not add any label.s
args: A named list of arguments to be passed to plot2D if the PCA coordinates are to be calculated. Ignored if the PCA coordinates are passed directly, i.e. object is a matrix.
...: Additional parameters passed to points or text (when labels is TRUE) when adding to plot2D, or spheres3d or text3d when adding the plot3D
radius: Radius of the spheres to be added to the visualisation produced by plot3D. Default is 0.3 (i.e plot3D's radius1 * 3), to emphasise the features with regard to uknown (radius1 = 0.1) and marker (radius1 * 2) features.

Value

NULL; used for its side effects.

Author

Laurent Gatto

Examples

library("pRolocdata")
data("tan2009r1")
x <- FeaturesOfInterest(description = "A test set of features of interest",
                        fnames = featureNames(tan2009r1)[1:10],
                        object = tan2009r1)

## using FeaturesOfInterest or feature names
par(mfrow = c(2, 1))
plot2D(tan2009r1)
highlightOnPlot(tan2009r1, x)
plot2D(tan2009r1)
highlightOnPlot(tan2009r1, featureNames(tan2009r1)[1:10])


.pca <- plot2D(tan2009r1)
head(.pca)
#>        PC1 (58.53%) PC2 (29.96%)
#> P20353    0.2103374    1.6959266
#> P53501   -0.4940607    1.6207386
#> Q7KU78   -1.1794311   -0.7242185
#> P04412    0.6128549    0.3944441
#> Q7KJ73    0.1866756   -0.3007028
#> Q7JZN0    2.0228016   -1.3087061
highlightOnPlot(.pca, x, col = "red")
highlightOnPlot(tan2009r1, x, col = "red", cex = 1.5)
highlightOnPlot(tan2009r1, x, labels = TRUE)

.pca <- plot2D(tan2009r1, dims = c(1, 3))
highlightOnPlot(.pca, x, pch = "+", dims = c(1, 3))
#> Warning: "dims" is not a graphical parameter
highlightOnPlot(tan2009r1, x, args = list(dims = c(1, 3)))


.pca2 <- plot2D(tan2009r1, mirrorX = TRUE, dims = c(1, 3))
## previous pca matrix, need to mirror X axis
highlightOnPlot(.pca, x, pch = "+", args = list(mirrorX = TRUE))
## new pca matrix, with X mirrors (and 1st and 3rd PCs)
highlightOnPlot(.pca2, x, col = "red")

plot2D(tan2009r1)
highlightOnPlot(tan2009r1, x)
highlightOnPlot(tan2009r1, x, labels = TRUE, pos = 3)
highlightOnPlot(tan2009r1, x, labels = "Flybase.Symbol", pos = 1)


## in 3 dimensions
if (interactive()) {
  plot3D(tan2009r1, radius1 = 0.05)
  highlightOnPlot3D(tan2009r1, x, labels = TRUE)
  highlightOnPlot3D(tan2009r1, x)
}