Primary interface, use this function to kick off the pipeline.
Usage
GSEPD_Process(GSEPD)
Arguments
GSEPD
The initialized GSEPD master object to operate on.
Details
Runs the pipeline. If any files are already present matching the generated filenames, they will be reused. If you changed a parameter that would alter the generated filenames, new ones are created. If a customization parameter is not part of the filename (like a p-value cutoff), you should change the output folder to keep new files separate.
Value
Returns the GSEPD object post-processed, for use in further plotting functions. Optional.
See Also
GSEPD_INIT
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 ) #would run DESeq2 and GOSeq and GSEPD comparing conditions A and B
Results
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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_Process.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GSEPD_Process
> ### Title: Processing
> ### Aliases: GSEPD_Process
>
> ### ** 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 ) #would run DESeq2 and GOSeq and GSEPD comparing conditions A and B
>
>
>
>
>
>
> dev.off()
null device
1
>