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Last data update: 2014.03.03

R: Principal components analysis of an spectral counts matrix.

counts.pca

R Documentation

Principal components analysis of an spectral counts matrix.

Description

A summary and different plots are given as a result of principal components analysis of an spectral counts matrix.

Usage

counts.pca(msnset, facs = NULL, do.plot = TRUE, snms = NULL, wait = TRUE)

Arguments

`msnset`	A MSnSet with spectral counts in the expression matrix.
`do.plot`	A logical indicating whether to plot the PCA PC1/PC2 map.
`facs`	NULL or a data frame with factors. See details below.
`snms`	Character vector with sample short names to be plotted. If NULL then 'Xnn' is plotted where 'nn' is the column number in the datset.
`wait`	This function may draw different plots, one by given factor in `facs`. When in interactive mode the default is to wait for confirmation before proceeding to the next plot. When `wait` is FALSE and R in interactive mode, instructs not to wait for confirmation.

Details

The spectral counts matrix is decomposed by means of prcomp. If do.plot is TRUE, a plot is generated for each factor showing the PC1/PC2 samples map, with samples colored as per factor level. If facs is NULL then the factors are taken from pData(msnset).

Value

Invisibly returns a list with values:

`pca`	The return value obtained from `prcomp`.
`pc.vars`	The percentage of variability corresponding to each principal component.

Author(s)

Josep Gregori

Examples

data(msms.dataset)
msnset <- pp.msms.data(msms.dataset)
lst <- counts.pca(msnset)
str(lst)
print(lst$pc.vars[,1:4])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(msmsEDA)
Loading required package: 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/msmsEDA/counts.pca.Rd_%03d_medium.png", width=480, height=480)
> ### Name: counts.pca
> ### Title: Principal components analysis of an spectral counts matrix.
> ### Aliases: counts.pca
> ### Keywords: hplot multivariate
> 
> ### ** Examples
> 
> data(msms.dataset)
> msnset <- pp.msms.data(msms.dataset)
> lst <- counts.pca(msnset)
> str(lst)
List of 2
 $ pca    :List of 5
  ..$ sdev    : num [1:14] 163.8 46.1 32.3 22.8 19.3 ...
  ..$ rotation: num [1:675, 1:14] 0.1744 0.2494 -0.0117 0.318 0.7473 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:675] "YJR104C" "YKL060C" "YDR155C" "YGR192C" ...
  .. .. ..$ : chr [1:14] "PC1" "PC2" "PC3" "PC4" ...
  ..$ center  : Named num [1:675] 221 208 173 214 244 ...
  .. ..- attr(*, "names")= chr [1:675] "YJR104C" "YKL060C" "YDR155C" "YGR192C" ...
  ..$ scale   : logi FALSE
  ..$ x       : num [1:14, 1:14] -120.3 -99.9 -127.4 -166 -127.2 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:14] "U2.2502.1" "U2.2502.2" "U2.2502.3" "U2.2502.4" ...
  .. .. ..$ : chr [1:14] "PC1" "PC2" "PC3" "PC4" ...
  ..- attr(*, "class")= chr "prcomp"
 $ pc.vars: num [1:3, 1:14] 163.8201 0.8414 0.8414 46.1183 0.0667 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:3] "Standard deviation" "Proportion of Variance" "Cumulative Proportion"
  .. ..$ : chr [1:14] "PC1" "PC2" "PC3" "PC4" ...
> print(lst$pc.vars[,1:4])
                             PC1      PC2      PC3      PC4
Standard deviation     163.82011 46.11827 32.26952 22.75380
Proportion of Variance   0.84136  0.06668  0.03265  0.01623
Cumulative Proportion    0.84136  0.90804  0.94068  0.95691
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>