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)

## Arguments

x 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).

## Value

A new average MSnSet.

## Details

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 NAs). This may change in the future.

compfnames to compare MSnSet feature names.

Laurent Gatto

## Examples

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
#>             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")
#>
#> Attaching package: ‘annotate’
#> The following object is masked from ‘package:mzR’:
#>
#>     nChrom
#>
#> This is pRoloc version 1.33.0
#>   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 = " - ")))