[Bioc-devel] Is there a way to change the name of R package that is already published on bioconductor?

Marcin -- I join with Herve in strongly discouraging you from this approach. Because you wish only to change upper versus lower case of your package name, and because some operating systems ignore case, your package will go through a release where it is not available under either upper- or lower-case variant. This will confuse and alienate your users, even more than changing the package name to something completely different. There are a number of packages, including some of our own, where the name could have been chosen more carefully, but we have learned to live with our changes of mind. After all, you can still claim to be consistent with the naming convention of the RTCGA.clinical and RTCGA.mutations package ;)

I guess Dario was referring to

   library(AnnotationHub)
   hub = AnnotationHub()
   eset = query(hub, "GSE62944")[[1]]

with

eset

ExpressionSet (storageMode: lockedEnvironment)
assayData: 23368 features, 7706 samples
   element names: exprs
protocolData: none
phenoData
   sampleNames: TCGA-02-0047-01A-01R-1849-01
     TCGA-02-0055-01A-01R-1849-01 ... TCGA-ZG-A8QZ-01A-11R-A37L-07 (7706
     total)
   varLabels: bcr_patient_barcode bcr_patient_uuid ... CancerType (421
     total)
   varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation:

table(eset$CancerType)

BLCA BRCA COAD  GBM HNSC KICH KIRC KIRP LAML  LGG LIHC LUAD LUSC   OV PRAD READ
  273 1082  468  170  481   66  540  226  164  528  212  514  490  344  423  164
SKCM STAD THCA UCEC
  373  146  506  536

print(object.size(eset), units="auto")

264.5 Mb

ov = eset[, eset$CancerType == "OV"]   ## ovarian samples

The data are Rsubread summarized counts from before the May update. In the near term, we are actively expanding offerings derived from that GSE to include the May update; this is in conjunction with efforts to develop an 'ExperimentHub' analog of AnnotationHub, for more experiment-centric, heavily curated resources.

It is a little unclear whether the AnnotationHub and your data are redundant or complementary, and whether they can be combined into a single offering. One philosophical difference is the use of semantically rich and integrated ExpressionSet versus basic data structures (data.frame, in your case). We also differ in when we separate data into cancer types; we opted for the entire data set because it is not impossibly large. And our data are AnnotationHub-based rather than package-based. Obviously, avoiding redundant access to the same data is beneficial. One possibility is to collaboratively curate the data into AnnotationHub / ExperimentHub resources, and to tailor access via packages that reference the resource (e.g., one can retrieve the GRASP2 data base through AnnotationHub as resource AH21414, or via grasp2db::GRASP2(); the latter comes with documentation for manipulating the resource).

It seems like there are similar opportunities for collaboration and reduced redundancy between the RTCGA, RTCGAToolbox, and TCGAbiolinks packages.

Martin Morgan
Bioconductor