After processing the pipeline, users may want to have further PCA figures generated. This function takes a completed GSEPD object and generates informative figures. This function includes parameters to specify a particular GO-Term of interest.
Usage
GSEPD_PCA_Spec(GSEPD, GOT, MDATA = NULL)
Arguments
GSEPD
The master GSEPD object, post-processed.
GOT
The GO-Term you'd like to specifically analyse. It should be found in the .MERGE file.
MDATA
Optionally, pass in the .MERGE dataset, if missing, we'll try to read the already-processed file from the output directory. This option exists because reading that file repeatedly is quite slow, so you're recommended to read it in once in advance if you intend on making more than one GO-Term specific plot.
Value
No return value. Generates files.
Note
This function uses either princomp() or prcomp() as neccesary, depending on sample count vs gene count.
See Also
GSEPD_PCA_Plot
Examples
data("IlluminaBodymap")
data("IlluminaBodymapMeta")
set.seed(1000) #fixed randomness
isoform_ids <- Name_to_RefSeq(c("HIF1A","EGFR","MYH7","CD33","BRCA2"))
rows_of_interest <- unique( c( isoform_ids ,
sample(rownames(IlluminaBodymap),
size=500,replace=FALSE)))
G <- GSEPD_INIT(Output_Folder="OUT",
finalCounts=round(IlluminaBodymap[rows_of_interest , ]),
sampleMeta=IlluminaBodymapMeta,
COLORS=c("green","black","red"))
G <- GSEPD_ChangeConditions( G, c("A","B")) #set testing groups first!
G <- GSEPD_Process( G ) #have to have processed results to plot them
GOT <- "GO:0012345" # specify a GO Term you'd like to review
#it should be present in the MERGE file.
MergeFile <- list.files(G$Output_Folder, pattern="MERGE")[1]
MDATA<-read.csv(sprintf("%s%s%s", G$Output_Folder, .Platform$file.sep, MergeFile),
as.is=TRUE,header=TRUE)
GOT=MDATA$category[1] #choose a GO term that is definitely in the output data.
GSEPD_PCA_Spec(G, GOT,MDATA=MDATA)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(rgsepd)
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4
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
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
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: goseq
Loading required package: BiasedUrn
Loading required package: geneLenDataBase
Loading R/GSEPD 1.4.2
Building human gene name caches
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/rgsepd/GSEPD_PCA_Spec.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GSEPD_PCA_Spec
> ### Title: Specialized PCA Plot
> ### Aliases: GSEPD_PCA_Spec
>
> ### ** Examples
>
> data("IlluminaBodymap")
> data("IlluminaBodymapMeta")
> set.seed(1000) #fixed randomness
> isoform_ids <- Name_to_RefSeq(c("HIF1A","EGFR","MYH7","CD33","BRCA2"))
> rows_of_interest <- unique( c( isoform_ids ,
+ sample(rownames(IlluminaBodymap),
+ size=500,replace=FALSE)))
> G <- GSEPD_INIT(Output_Folder="OUT",
+ finalCounts=round(IlluminaBodymap[rows_of_interest , ]),
+ sampleMeta=IlluminaBodymapMeta,
+ COLORS=c("green","black","red"))
Keeping rows with counts (458 of 505)
> G <- GSEPD_ChangeConditions( G, c("A","B")) #set testing groups first!
> G <- GSEPD_Process( G ) #have to have processed results to plot them
converting counts to integer mode
Would generate OUT/DESEQ.counts.Ax4.Bx8.csv, but one is already present.
Generating OUT/GSEPD.HMA.Ax4.Bx8.csv, overwriting previous existing version.
All Done!
>
> GOT <- "GO:0012345" # specify a GO Term you'd like to review
>
> #it should be present in the MERGE file.
> MergeFile <- list.files(G$Output_Folder, pattern="MERGE")[1]
> MDATA<-read.csv(sprintf("%s%s%s", G$Output_Folder, .Platform$file.sep, MergeFile),
+ as.is=TRUE,header=TRUE)
>
> GOT=MDATA$category[1] #choose a GO term that is definitely in the output data.
>
> GSEPD_PCA_Spec(G, GOT,MDATA=MDATA)
>
>
>
>
>
> dev.off()
null device
1
>