Given a list of MSnSet
instances, typically representing
replicated experiments, the function returns an average
MSnSet
.
averageMSnSet(x, avg = function(x) mean(x, na.rm = TRUE), disp = npcv)
A list
of valid MSnSet
instances to be averaged.
The averaging function. Default is the mean after
removing missing values, as computed by function(x)
mean(x, na.rm = TRUE)
.
The disperion function. Default is an non-parametric
coefficient of variation that replaces the standard deviation by
the median absolute deviation as computed by
mad(x)/abs(mean(x))
. See npcv
for
details. Note that the mad
of a single value is 0 (as
opposed to NA
for the standard deviation, see example
below).
A new average MSnSet
.
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.
compfnames
to compare MSnSet feature names.
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)
#> [1] 1311 4
head(exprs(avg))
#> X114 X115 X116 X117
#> P20353 0.3605000 0.3035000 0.2095000 0.1265000
#> P53501 0.4299090 0.1779700 0.2068280 0.1852625
#> Q7KU78 0.1704443 0.1234443 0.1772223 0.5290000
#> P04412 0.2567500 0.2210000 0.3015000 0.2205000
#> Q7KJ73 0.2160000 0.1830000 0.3420000 0.2590000
#> Q7JZN0 0.0965000 0.2509443 0.4771667 0.1750557
head(fData(avg)$nNA)
#> X114 X115 X116 X117
#> P20353 1 1 1 1
#> P53501 1 1 1 1
#> Q7KU78 0 0 0 0
#> P04412 1 1 1 1
#> Q7KJ73 2 2 2 2
#> Q7JZN0 0 0 0 0
head(fData(avg)$disp)
#> X114 X115 X116 X117
#> P20353 0.076083495 0.1099127 0.109691169 0.14650198
#> P53501 0.034172542 0.2640556 0.005139653 0.17104568
#> Q7KU78 0.023198743 0.4483795 0.027883087 0.04764499
#> P04412 0.053414021 0.2146751 0.090972139 0.27903810
#> Q7KJ73 0.000000000 0.0000000 0.000000000 0.00000000
#> Q7JZN0 0.007681865 0.1959534 0.097873350 0.06210542
## using the standard deviation as measure of dispersion
avg2 <-averageMSnSet(x, disp = sd)
head(fData(avg2)$disp)
#> X114 X115 X116 X117
#> P20353 NA NA NA NA
#> P53501 NA NA NA NA
#> Q7KU78 0.03158988 0.04750696 0.01033500 0.04667976
#> P04412 NA NA NA NA
#> Q7KJ73 NA NA NA NA
#> Q7JZN0 0.02121910 0.04160155 0.08447534 0.04988318
## keep only complete observations, i.e proteins
## that had 0 missing values for all samples
sel <- apply(fData(avg)$nNA, 1 , function(x) all(x == 0))
avg <- avg[sel, ]
disp <- rowMax(fData(avg)$disp)
library("pRoloc")
#> Loading required package: MLInterfaces
#> Loading required package: annotate
#> Loading required package: AnnotationDbi
#> Loading required package: XML
#>
#> Attaching package: ‘annotate’
#> The following object is masked from ‘package:mzR’:
#>
#> nChrom
#> Loading required package: cluster
#> Loading required package: BiocParallel
#>
#> This is pRoloc version 1.43.2
#> Visit https://lgatto.github.io/pRoloc/ to get started.
setStockcol(paste0(getStockcol(), "AA"))
plot2D(avg, cex = 7.7 * disp)
title(main = paste("Dispersion: non-parametric CV",
paste(round(range(disp), 3), collapse = " - ")))