bandle-plots-translocations: Plot changes in localisation between two conditions/datasets

In ococrook/bandle: An R package for the Bayesian analysis of differential subcellular localisation experiments

Plot changes in localisation between two conditions/datasets

Description

This function produces a chord diagram (also known as a circos plot) or an alluvial plot (also known as a Sankey diagram) to show changes in location between two conditions or datasets.

Usage

plotTranslocations(
  params,
  type = "alluvial",
  all = FALSE,
  fcol,
  col,
  labels = TRUE,
  labels.par = "adj",
  cex = 1,
  spacer = 4,
  ...
)

Arguments

params	An instance of class bandleParams or an instance of class MSnSetList of length 2.
type	A character specifying the type of visualisation to plot. One of "alluvial" (default) or "chord".
all	A logical specifying whether to count all proteins or only show those that have changed in location between conditions. Default is FALSE.
fcol	If params is a list of MSnSets. Then fcol must be defined. This is a character vector of length 2 to set different labels for each dataset. If only one label is specified, and the character is of length 1 then this single label will be used to identify the annotation column in both datasets.
col	A list of colours to define the classes in the data. If not defined then the default pRoloc colours in getStockCol() are used.
labels	A logical indicating whether to display class/organelle labels for the chord segments or alluvial stratum. Default is TRUE.
labels.par	If type is "alluvial". Label style can be specified as one of "adj", "repel". Default is "adj".
cex	Text size. Default is 1.
spacer	A numeric. Default is 4. Controls the white space around the circos plotting region.
...	Additional arguments passed to the chordDiagram function.

Value

Returns a directional circos/chord diagram showing the translocation of proteins between conditions. If type = "alluvial" ouput is a ggplot object.

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 1, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process the results
bandleres <- bandleProcess(res)

## plot the results
plotTranslocations(bandleres)
plotTranslocations(bandleres, type = "chord")

ococrook/bandle documentation built on Nov. 4, 2024, 12:27 a.m.