ExperimentHubData provides tools to add or modify resources in Bioconductor’s ExperimentHub. This ‘hub’ houses curated data from courses, publications or experiments. The resources are generally not files of raw data (as can be the case in AnnotationHub) but instead are R / Bioconductor objects such as GRanges, SummarizedExperiment, data.frame etc. Each resource has associated metadata that can be searched through the ExperimentHub client interface.
Resources are contributed to ExperimentHub in the form of a package. The package contains the resource metadata, man pages, vignette and any supporting R functions the author wants to provide. This is a similar design to the existing Bioconductor experimental data packages except the data are stored in AWS S3 buckets instead of the data/ directory of the package.
Below are the steps required for adding new resources.
Bioconductor team memberThe man page and vignette examples in the software package will not work until the data are available in ExperimentHub. Adding the data to AWS S3 and the metadata to the production database involves assistance from a Bioconductor team member. If you are interested in submitting a package, please send an email to packages@bioconductor.org so a team member can work with you through the process.
When a resource is downloaded from ExperimentHub the associated software package is loaded in the workspace making the man pages and vignettes readily available. Because documentation plays an important role in understanding these curated resources please take the time to develop clear man pages and a detailed vignette. These documents provide essential background to the user and guide appropriate use the of resources.
Below is an outline of package organization. The files listed are required unless otherwise stated.
ExperimentHub database. The file should be generated from the code in inst/scripts/make-metadata.R where the final data are written out with write.csv(…, row.names=FALSE). The required column names and data types are specified in AnnotationHubData::readMetadataFromCsv(). See ?readMetadataFromCsv for details.make-data.R: A script describing the steps involved in making the data object(s). This includes where the original data were downloaded from, pre-processing, and how the final R object was made. Include a description of any steps performed outside of R with third party software. Data objects should be serialized with save() with the .rda extension on the filename.
make-metadata.R: A script to make the metadata.csv file located in inst/extdata of the package. See ?readMetadataFromCsv for a description of expected fields and data types. readMetadataFromCsv() can be used to validate the metadata.csv file before submitting the package.
vignettes/
One or more vignettes describing analysis workflows.
R/
zzz.R: Optional. You can include a .onLoad() function in a zzz.R file that exports each resource name (i.e., title) into a function. This allows the data to be loaded by name, e.g., resouce123().
.onLoad <- function(libname, pkgname) {
fl <- system.file("extdata", "metadata.csv", package=pkgname)
titles <- read.csv(fl, stringsAsFactors=FALSE)$Title
createHubAccessors(pkgname, titles)
}Internal detail is in ExperimentHub::createHubAccessors and ExperimentHub:::.hubAccessorFactory funtions. The resource-named function has a single ‘metadata’ argument. When metadata=TRUE, the metadata are loaded (equivalent to single-bracket method on an ExperimentHub object) and when FALSE the full resource is loaded (equivalent to double-bracket method).
Optional functions to enhance data exploration.
package man page: The package man page serves as a landing point and should briefly describe all resources associated with the package. There should be an entry for each resource title either on the package man page or individual man pages.
resource man pages: Resources can be documented on the same page, grouped by common type or have their own dedicated man pages.
document how data are loaded: Data can be accessed via the standard ExperimentHub interface with single and double-bracket methods, e.g.,
library(ExperimentHub) eh <- ExperimentHub() myfiles <- query(eh, "PACKAGENAME") myfiles[[1]] ## load the first resource in the list myfiles[["EH123"]] ## load by EH id
If a .onLoad() function is used to export each resource as a function also document that method of loading, e.g.,
resourceA(meta = FALSE) ## data are loaded resourceA(meta = TRUE) ## metadata are displayed
DESCRIPTION / NAMESPACE
The package should depend on and fully import ExperimentHub. If using the suggested .onLoad() function, import the utils package in the DESCRIPTION file and selectively importFrom(utils, read.csv) in the NAMESPACE.
Package authors are encouraged to use the ExperimentHub::listResources() and ExperimentHub::loadResource() functions in their man pages and vignette. These helpers are designed to facilitate data discovery within a specific package vs within all of ExperimentHub.
Data are not formally part of the software package and are stored separately in AWS S3 buckets. The author should make the data available via dropbox, ftp or another mutually accessible application and it will be uploaded to S3 by a member of the Bioconductor team.
Data files should be created with save() and have the .rda extension.
When you are satisfied with the representation of your resources in make-metadata.R (which produces metadata.csv) the Bioconductor team member will add the metadata to the production database.
Once the data are in AWS S3 and the metadata have been added to the production database the man pages and vignette can be finalized. When the package passes R CMD build and check it can be submitted to the package tracker for review.
Metadata for new versions of the data can be added to the same package as they become available.
The titles for the new versions must be unique and not match the title of any resource currently in AnnotationHub. Good practice would be to include the version and / or genome build in the title.
Make data available via dropbox, ftp, etc. and notify maintainer@bioconductor.org
Update make-metadata.R with the new metadata information
Generate a new or updated metadata.csv file. The package should contain metadata for all versions of the data in AnnotationHub. When adding a new version it might be helpful to write a new csv file named by version, e.g., metadata_v84.csv, metadata_85.csv etc.
Bump package version and commit to svn/git
Notify maintainer@bioconductor.org that an update is ready and a team member will add the new metadata to the production database; new resources will not be visible in ExperimentHub until the metadata are added to the database.
Contact maintainer@bioconductor.org with any questions. # Bug fixes
A bug fix may involve a change to the metadata, data resource or both.
The replacement resource must have the same name as the original
Notify maintainer@bioconductor.org that you want to replace the data and make the files available via dropbox, ftp, etc.
New metadata records can be added for new resources but modifying existing records is discouraged. Record modification will only be done in the case of bug fixes.
Notify maintainer@bioconductor.org that you want to change the metadata
Update make-metadata.R with modified information
Bump the package version and commit to svn/git
Removing resources should be done with caution. The intent is that ExperimentHub be a ‘reproducible’ resource by providing a stable snapshot of the data. Data made available in Bioconductor version x.y.z should be available for all versions greater than x.y.z. Unfortunately this is not always possible. If you find it necessary to remove data from ExperimentHub please contact maintainer@bioconductor.org for assistance.
When a resource is removed from ExperimentHub the ‘status’ field in the metadata is modified to explain why they are no longer available. Once this status is changed the ExperimentHub() constructor will not list the resource among the available ids. An attempt to extract the resource with ‘[[’ and the EH id will return an error along with the status message.
ExperimentHub_dockerThe ExperimentHub_docker offers an isolated test environment for inserting / extracting metadata records in the ExperimentHub database. The README in the package explains how to set up the Docker and inserting records is done with ExperimentHub::addResources().
In general this level of testing should not be necessary when submitting a package with new resources. The best way to validate record metadata is to read inst/extdata/metadata.csv with ExperimentHubData::readMetadataFromCsv(). If that is successful the metadata are ready to go.