a list of 2 data.frames:
one with the number of times each codon type is found in each ORF and
one with the number of reads for each codon in each ORF.
typeData
a character string. It is used as title for the PCA.
Ex. typeData="codonCoverage"
Value
a list of length 2:
PCA_scores - matrix of the scores on the first 4 principal components.
PCA_plots - a list of 5 PCA scatterplots.
Examples
#How to perform a PCA analysis based on codon coverage
#adapted from
#http://stackoverflow.com/questions/20260434/test-significance-of-clusters-on-a-pca-plot
#either get the codon frequency, coverage, and annotation using a function
#such as codonInfo in this package
#or create a list of matrices with the above information
data(codonDataCtrl)
codonData <- codonDataCtrl
codonUsage <- codonData[[1]]
codonCovMatrix <- codonData[[2]]
#keep only genes with a minimum number of reads
nbrReadsGene <- apply(codonCovMatrix, 1, sum)
ixExpGenes <- which(nbrReadsGene >= 50)
codonCovMatrix <- codonCovMatrix[ixExpGenes, ]
#get the PCA on the codon coverage
codonCovMatrixTransp <- t(codonCovMatrix)
rownames(codonCovMatrixTransp) <- colnames(codonCovMatrix)
colnames(codonCovMatrixTransp) <- rownames(codonCovMatrix)
listPCACodonCoverage <- codonPCA(codonCovMatrixTransp,"codonCoverage")
print(listPCACodonCoverage[[2]])
#See aditional examples in the pdf manual
Results
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> library(RiboProfiling)
Loading required package: Biostrings
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: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: XVector
Warning messages:
1: replacing previous import 'BiocGenerics::Position' by 'ggplot2::Position' when loading 'RiboProfiling'
2: replacing previous import 'ggplot2::Position' by 'BiocGenerics::Position' when loading 'ggbio'
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RiboProfiling/codonPCA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: codonPCA
> ### Title: PCA graphs on codon coverage
> ### Aliases: codonPCA
>
> ### ** Examples
>
> #How to perform a PCA analysis based on codon coverage
> #adapted from
> #http://stackoverflow.com/questions/20260434/test-significance-of-clusters-on-a-pca-plot
> #either get the codon frequency, coverage, and annotation using a function
> #such as codonInfo in this package
> #or create a list of matrices with the above information
> data(codonDataCtrl)
> codonData <- codonDataCtrl
> codonUsage <- codonData[[1]]
> codonCovMatrix <- codonData[[2]]
>
> #keep only genes with a minimum number of reads
> nbrReadsGene <- apply(codonCovMatrix, 1, sum)
> ixExpGenes <- which(nbrReadsGene >= 50)
> codonCovMatrix <- codonCovMatrix[ixExpGenes, ]
>
> #get the PCA on the codon coverage
> codonCovMatrixTransp <- t(codonCovMatrix)
> rownames(codonCovMatrixTransp) <- colnames(codonCovMatrix)
> colnames(codonCovMatrixTransp) <- rownames(codonCovMatrix)
>
> listPCACodonCoverage <- codonPCA(codonCovMatrixTransp,"codonCoverage")
> print(listPCACodonCoverage[[2]])
$`PC_1-2`
$`PC_1-3`
$`PC_1-4`
$`PC_2-3`
$`PC_2-4`
> #See aditional examples in the pdf manual
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
