Given two MSnSet
instances of one MSnSetList
with at
least two items, this function produces an animation that shows
the transition from the first data to the second.
move2Ds(object, pcol, fcol = "markers", n = 25, hl)
An linkS4class{MSnSet}
or a
MSnSetList
. In the latter case, only the two first elements
of the list will be used for plotting and the others will be
silently ignored.
If object
is an MSnSet
, a factor
or the name of a phenotype variable (phenoData
slot)
defining how to split the single MSnSet
into two or more
data sets. Ignored if object
is a
MSnSetList
.
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.
Number of frames, Default is 25.
An optional instance of class
linkS4class{FeaturesOfInterest}
to track features of
interest.
Used for its side effect of producing a short animation.
plot2Ds
to a single figure with the two
datasets.
library("pRolocdata")
data(dunkley2006)
## Create a relevant MSnSetList using the dunkley2006 data
xx <- split(dunkley2006, "replicate")
xx1 <- xx[[1]]
xx2 <- xx[[2]]
fData(xx1)$markers[374] <- "Golgi"
fData(xx2)$markers[412] <- "unknown"
xx@x[[1]] <- xx1
xx@x[[2]] <- xx2
## The features we want to track
foi <- FeaturesOfInterest(description = "test",
fnames = featureNames(xx[[1]])[c(374, 412)])
## (1) visualise each experiment separately
par(mfrow = c(2, 1))
plot2D(xx[[1]], main = "condition A")
highlightOnPlot(xx[[1]], foi)
plot2D(xx[[2]], mirrorY = TRUE, main = "condition B")
highlightOnPlot(xx[[2]], foi, args = list(mirrorY = TRUE))
## (2) plot both data on the same plot
par(mfrow = c(1, 1))
tmp <- plot2Ds(xx)
highlightOnPlot(data1(tmp), foi, lwd = 2)
highlightOnPlot(data2(tmp), foi, pch = 5, lwd = 2)
## (3) create an animation
move2Ds(xx, pcol = "replicate")
move2Ds(xx, pcol = "replicate", hl = foi)