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Last data update: 2014.03.03

R: Generate an average 'MSnSet'

averageMSnSet

R Documentation

Generate an average `MSnSet`

Description

Given a list of MSnSet instances, typically representing replicated experiments, the function returns an average MSnSet.

Usage

averageMSnSet(x, avg = function(x) mean(x, na.rm = TRUE), disp = npcv)

Arguments

`x`	A `list` of valid `MSnSet` instances to be averaged.
`avg`	The averaging function. Default is the mean after removing missing values, as computed by `function(x) mean(x, na.rm = TRUE)`.
`disp`	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).

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.

Value

A new average MSnSet.

Author(s)

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)
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: non-parametric CV",
                   paste(round(range(disp), 3), collapse = " - ")))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MSnbase)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: mzR
Loading required package: Rcpp
Loading required package: BiocParallel
Loading required package: ProtGenerics

This is MSnbase version 1.20.7 
  Read '?MSnbase' and references therein for information
  about the package and how to get started.


Attaching package: 'MSnbase'

The following object is masked from 'package:stats':

    smooth

The following object is masked from 'package:base':

    trimws

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MSnbase/averageMSnSet.Rd_%03d_medium.png", width=480, height=480)
> ### Name: averageMSnSet
> ### Title: Generate an average 'MSnSet'
> ### Aliases: averageMSnSet
> 
> ### ** Examples
> 
> library("pRolocdata")

This is pRolocdata version 1.10.0.
Use 'pRolocdata()' to list available data sets.
> ## 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: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: XML
Loading required package: cluster

This is pRoloc version 1.12.4 
  Read '?pRoloc' and references therein for information
  about the package and how 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 = " - ")))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>

Generate an average MSnSet