Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/averageMSnSet.R
Given a list of MSnSet
instances, typically representing
replicated experiments, the function returns an average
MSnSet
.
1  averageMSnSet(x, avg = function(x) mean(x, na.rm = TRUE), disp = npcv)

x 
A 
avg 
The averaging function. Default is the mean after
removing missing values, as computed by 
disp 
The disperion function. Default is an nonparametric
coefficient of variation that replaces the standard deviation by
the median absolute deviation as computed by

This function is aimed at facilitating the visualisation of replicated experiments and should not be used as a replacement for a statistical analysis.
The samples of the instances to be averaged must be identical but
can be in a different order (they will be reordered by
default). The features names of the result will correspond to the
union of the feature names of the input MSnSet
instances. Each average value will be computed by the avg
function and the dispersion of the replicated measurements will be
estimated by the disp
function. These dispersions will be
stored as a data.frame
in the feature metadata that can be
accessed with fData(.)$disp
. Similarly, the number of
missing values that were present when average (and dispersion)
were computed are available in fData(.)$disp
.
Currently, the feature metadata of the returned object corresponds
the the feature metadata of the first object in the list
(augmented with the missing value and dispersion values); the
metadata of the features that were missing in this first input are
missing (i.e. populated with NA
s). This may change in the
future.
A new average MSnSet
.
Laurent Gatto
compfnames
to compare MSnSet feature names.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  library("pRolocdata")
## 3 replicates from Tan et al. 2009
data(tan2009r1)
data(tan2009r2)
data(tan2009r3)
x < MSnSetList(list(tan2009r1, tan2009r2, tan2009r3))
avg < averageMSnSet(x)
dim(avg)
head(exprs(avg))
head(fData(avg)$nNA)
head(fData(avg)$disp)
## using the standard deviation as measure of dispersion
avg2 <averageMSnSet(x, disp = sd)
head(fData(avg2)$disp)
## keep only complete observations, i.e proteins
## that had 0 missing values for all samples
sel < apply(fData(avg)$nNA, 1 , function(x) all(x == 0))
avg < avg[sel, ]
disp < rowMax(fData(avg)$disp)
library("pRoloc")
setStockcol(paste0(getStockcol(), "AA"))
plot2D(avg, cex = 7.7 * disp)
title(main = paste("Dispersion: nonparametric CV",
paste(round(range(disp), 3), collapse = "  ")))

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