---
title: "Introduction to ExperimentHubData"
author: "Valerie Obenchain"
date: "Modified: October 2016. Compiled: `r format(Sys.Date(), '%d %b %Y')`"
output:
BiocStyle::html_document:
toc: true
vignette: >
%\VignetteIndexEntry{Introduction to ExperimentHubData}
%\VignetteEngine{knitr::rmarkdown}
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```
# Overview
`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.
# New resources
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.
## Notify `Bioconductor` team member
The 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.
## Building the software package
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.
* inst/extdata/
- metadata.csv:
This file contains the metadata in the format of one row per resource
to be added to the `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.
* inst/scripts/
- 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:
This sample .onLoad() function in a zzz.R file makes each resource name
(i.e., title) into a function which allows the data to be loaded by name.
Substitute the name of your package in place of PACKAGENAME below.
```
.onLoad <- function(libname, pkgname) {
titles <- read.csv(system.file("extdata", "metadata.csv",
package="PACKAGENAME"),
stringsAsFactors=FALSE)$Title
rda <- gsub(".rda", "", titles, fixed=TRUE)
if (!length(rda))
stop("no .rda objects found in metadata")
## Functions to load resources by name:
ns <- asNamespace(pkgname)
sapply(rda,
function(xx) {
func = function(metadata = FALSE) {
if (!isNamespaceLoaded("ExperimentHub"))
attachNamespace("ExperimentHub")
eh <- query(ExperimentHub(), "PACKAGENAME")
ehid <- names(query(eh, xx))
if (!length(ehid))
stop(paste0("resource ", xx,
"not found in ExperimentHub"))
if (metadata)
eh[ehid]
else eh[[ehid]]
}
assign(xx, func, envir=ns)
namespaceExport(ns, xx)
})
}
```
- Optional functions to enhance data exploration.
* man/
- 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 \alias
entry for each resource title either on the package man page or individual
man pages.
- resource man pages:
It is recommended (but not required) that each resource have a dedicated
man page.
- document data loading:
When the package is loaded the zzz.R file makes the title of each
resource into a function with a single argument, 'metadata'. The man
page should document that data can be loaded by name, e.g.,
```
resourceA(meta = FALSE) ## data are loaded into the workspace
resourceA(meta = TRUE) ## only the metadata are displayed
```
Optionally, the data can be loaded through the ExperimentHub interface.
```
library(ExperimentHub)
eh <- ExperimentHub()
myfiles <- query(eh, "PACKAGENAME")
myfiles[[1]] ## load the first resource in the list
```
* DESCRIPTION / NAMESPACE
The package should depend on and fully import ExperimentHub.
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 objects
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.
## Metadata
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.
## Package review
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](https://github.com/Bioconductor/Contributions) for
review.
# Add additional resources
Multiple versions of the data can be added to the same package as they
become available. Be sure the title is descriptive and reflects the
distinguishing information such as genome build. Adding new resources
to an existing package requires the following steps:
* make data available via dropbox, ftp, etc. and notify
maintainer@bioconductor.org; if you have access to the AWS S3 buckeet
you can upload the files yourself
* update make-metadata.R with the new metadata information
* re-generate the metadata.csv file
* 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.
# Bug fixes
A bug fix may involve a change to the metadata, data resource or both.
## Update the resource
* 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.
## Update the metadata
* notify maintainer@bioconductor.org that you want to change the metadata
* update make-metadata.R with modified information
* regenerate metadata.csv
* bump the package version and commit to svn/git
# Remove resources
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.
To remove a resource from ExperimentHub contact maintainer@bioconductor.org.
# `ExperimentHub_docker`
The [ExperimentHub_docker](https://github.com/Bioconductor/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.