plot2D: Plot organelle assignment data and results.
In pRoloc: A unifying bioinformatics framework for spatial proteomics

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

Generate 2 or 3 dimensional feature distribution plots to illustrate localistation clusters. Rows/features containing NA values are removed prior to dimension reduction except for the "nipals" method. For this method, it is advised to set the method argument 'ncomp' to a low number of dimensions to avoid computing all components when analysing large datasets.

plot2D(object, fcol = "markers", fpch, unknown = "unknown",
  dims = 1:2, score = 1, method = "PCA", methargs,
  axsSwitch = FALSE, mirrorX = FALSE, mirrorY = FALSE, col, pch, cex,
  index = FALSE, idx.cex = 0.75, addLegend, identify = FALSE,
  plot = TRUE, grid = TRUE, ...)

## S4 method for signature 'MSnSet'
plot3D(object, fcol = "markers", dims = c(1, 2, 3),
  radius1 = 0.1, radius2 = radius1 * 2, plot = TRUE, ...)

`object`	An instance of class `MSnSet`.
`fcol`	Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is `markers`. Use `NULL` to suppress any colouring.
`fpch`	Featre meta-data label (fData column name) desining the groups to be differentiated using different point symbols.
`unknown`	A `character` (default is `"unknown"`) defining how proteins of unknown/un-labelled localisation are labelled.
`dims`	A `numeric` of length 2 (or 3 for `plot3D`) defining the dimensions to be plotted. Defaults are `c(1, 2)` and `c(1, 2, 3)`. Always `1:2` for MDS.
`score`	A numeric specifying the minimum organelle assignment score to consider features to be assigned an organelle. (not yet implemented).
`method`	A `character` describe how to transform the data or what to plot. One of `"PCA"` (default), `"MDS"`, `"kpca"`, `"nipals"`, `"t-SNE"` or `"lda"`, defining what dimensionality reduction is applied: principal component analysis (see `prcomp`), classical multidimensional scaling (see `cmdscale`), kernel PCA (see `kpca`), nipals (principal component analysis by NIPALS, non-linear iterative partial least squares which support missing values; see `nipals`) t-SNE (see `Rtsne`) or linear discriminant analysis (see `lda`). The last method uses `fcol` to defined the sub-cellular clusters so that the ration between within ad between cluster variance is maximised. All the other methods are unsupervised and make use `fcol` only to annotate the plot. Prior to t-SNE, duplicated features are removed and a message informs the user if such filtering is needed. `"scree"` can also be used to produce a scree plot. `"hexbin"` applies PCA to the data and uses bivariate binning into hexagonal cells from `hexbin` to emphasise cluster density. If none is used, the data is plotted as is, i.e. without any transformation. In this case, `object` can either be an `MSnSet` or a `matrix` (as invisibly returned by `plot2D`). This enables to re-generate the figure without computing the dimensionality reduction over and over again, which can be time consuming for certain methods. If `object` is a `matrix`, an `MSnSet` containing the feature metadata must be provided in `methargs` (see below for details). Available methods are listed in `plot2Dmethods`.
`methargs`	A `list` of arguments to be passed when `method` is called. If missing, the data will be scaled and centred prior to PCA and t-SNE (i.e. `Rtsne`'s arguments `pca_center` and `pca_scale` are set to `TRUE`). If `method = "none"` and `object` is a `matrix`, then the first and only argument of `methargs` must be an `MSnSet` with matching features with `object`.
`axsSwitch`	A `logical` indicating whether the axes should be switched.
`mirrorX`	A `logical` indicating whether the x axis should be mirrored?
`mirrorY`	A `logical` indicating whether the y axis should be mirrored?
`col`	A `character` of appropriate length defining colours.
`pch`	A `character` of appropriate length defining point character.
`cex`	Character expansion.
`index`	A `logical` (default is `FALSE`, indicating of the feature indices should be plotted on top of the symbols.
`idx.cex`	A `numeric` specifying the character expansion (default is 0.75) for the feature indices. Only relevant when `index` is TRUE.
`addLegend`	A character indicating where to add the legend. See `addLegend`for details. If missing (default), no legend is added.
`identify`	A logical (default is `TRUE`) defining if user interaction will be expected to identify individual data points on the plot. See also `identify`.
`plot`	A `logical` defining if the figure should be plotted. Useful when retrieving data only. Default is `TRUE`.
`grid`	A `logical` indicating whether a grid should be plotted. Default is `TRUE`.
`...`	Additional parameters passed to `plot` and `points`.
`radius1`	A `numeric` specifying the radius of feature of unknown localisation. Default is 0.1, which is specidied on the data scale. See `plot3d` for details.
`radius2`	A `numeric` specifying the radius of marker feature. Default is `radius` * 2.

plot3D relies on the ##' rgl package, that will be loaded automatically.

Note that plot2D has been update in version 1.3.6 to support more organelle classes than colours defined in getStockcol. In such cases, the default colours are recycled using the default plotting characters defined in getStockpch. See the example for an illustration. The alpha argument is also depreciated in version 1.3.6. Use setStockcol to set colours with transparency instead. See example below.
Version 1.11.3: to plot data as is, i.e. without any transformation, method can be set to "none" (as opposed to passing pre-computed values to method as a matrix, in previous versions). If object is an MSnSet, the untransformed values in the assay data will be plotted. If object is a matrix with coordinates, then a matching MSnSet must be passed to methargs.

Used for its side effects of generating a plot. Invisibly returns the 2 or 3 dimensions that are plotted.

Laurent Gatto <lg390@cam.ac.uk>

addLegend to add a legend to plot2D figures (the legend is added by default on plot3D) and plotDist for alternative graphical representation of quantitative organelle proteomics data. plot2Ds to overlay 2 data sets on the same PCA plot. The plotEllipse function can be used to visualise TAGM models on PCA plots with ellipses.

library("pRolocdata")
data(dunkley2006)
plot2D(dunkley2006, fcol = NULL)
plot2D(dunkley2006, fcol = NULL, col = "black")
plot2D(dunkley2006, fcol = "markers")
addLegend(dunkley2006,
          fcol = "markers",
          where = "topright",
          cex = 0.5, bty = "n", ncol = 3)
title(main = "plot2D example")
## available methods
plot2Dmethods
plot2D(dunkley2006, fcol = NULL, method = "kpca", col = "black")
plot2D(dunkley2006, fcol = NULL, method = "kpca", col = "black",
       methargs = list(kpar = list(sigma = 1)))
plot2D(dunkley2006, method = "lda")
plot2D(dunkley2006, method = "hexbin")
## Using transparent colours
setStockcol(paste0(getStockcol(), "80"))
plot2D(dunkley2006, fcol = "markers")
## New behavious in 1.3.6 when not enough colours
setStockcol(c("blue", "red", "green"))
getStockcol() ## only 3 colours to be recycled
getMarkers(dunkley2006)
plot2D(dunkley2006)
## reset colours
setStockcol(NULL)
plot2D(dunkley2006, method = "none") ## plotting along 2 first fractions
plot2D(dunkley2006, dims = c(3, 5), method = "none") ## plotting along fractions 3 and 5
## pre-calculate PC1 and PC2 coordinates
pca <- plot2D(dunkley2006, plot=FALSE)
head(pca)
plot2D(pca, method = "none", methargs  = list(dunkley2006))

## plotting in 3 dimenstions
plot3D(dunkley2006)
plot3D(dunkley2006, radius2 = 0.3)
plot3D(dunkley2006, dims = c(2, 4, 6))