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AnnotationHub.Rmd
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AnnotationHub.Rmd
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---
title: AnnotationHub
author: Kasper D. Hansen
bibliography: genbioconductor.bib
---
```{r front, child="front.Rmd", echo=FALSE}
```
## Dependencies
This document has the following dependencies:
```{r dependencies, warning=FALSE, message=FALSE}
library(AnnotationHub)
```
Use the following commands to install these packages in R.
```{r biocLite, eval=FALSE}
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("AnnotationHub"))
```
## Corrections
Improvements and corrections to this document can be submitted on its [GitHub](https://github.com/kasperdanielhansen/genbioconductor/blob/master/Rmd/AnnotationHub.Rmd) in its [repository](https://github.com/kasperdanielhansen/genbioconductor).
## Overview
Annotation information is extremely important for putting your data into context. There are many online resources for doing this, and many different databases organizes different information suing different approaches.
There are multiple ways to access annotation information in Bioconductor.
Here we discuss a new way of doing so, through the package `r Biocpkg("AnnotationHub")`. This package provides access to a ton of online resources through a unified interface. However, each data resource has its own peculiarities, so a user still needs to understand what the different datasets are.
In a recent paper I was involved in [@Hansen:EBV], I used `r Biocpkg("AnnotationHub")` to interrogate my data against all transcription factor data available through the ENCODE project. I managed to write the code and conduct the analysis in the matter of a single evening, which I think is pretty awesome.
## Other Resources
- The `r Biocpkg("AnnotationHub")` vignette from the [AnnotationHub webpage](http://bioconductor.org/packages/AnnotationHub).
- The [Annotation Resources](http://www.bioconductor.org/help/workflows/annotation/Annotation_Resources/) workflow on Bioconductor contains material on `r Biocpkg("AnnotationHub")`.
- The BioC 2015 conference had a tutorial on `r Biocpkg("AnnotationHub")`; material is available through the [course materials](http://www.bioconductor.org/help/course-materials/) site.
## Usage
First we create an `AnnotationHub` instance. The first time you do this, it will create a local cache on your system, so that repeat queries for the same information (even in different R sessions) will be very fast.
```{r annoHub,results="hide"}
ah <- AnnotationHub()
ah
```
As you can see, `ah` contains tons of information. The information content is constantly changing, which is why there is a `snapshotDate`. While the object is big, it actually only contains pointers to online information. Actually downloading all the resources available in an `AnnotationHub` is prohibitive.
The object is organized as a vector, with single-dimension indexing. You can get information about a single resource by indexing with a single `[`; using two brackets (`[[`) downloads the object:
```{r no1}
ah[1]
```
The way you use `r Biocpkg("AnnotationHub")` is by using various tools to narrow down your hub to a single or a small number of datasets. Then you download these datasets for your own usage.
Let us first explore some high-level features of the hub:
```{r look}
unique(ah$dataprovider)
unique(ah$rdataclass)
```
(we will discuss many of these data classes in future sessions).
You can narrow down the hub by using one (or more) of the following strategies
- Use `subset` (or `[`) to do a specific subsetting operation.
- Use `query` to do a command-line search over the metadata of the hub.
- Use `display` to get a Shiny interface in a browser, so you can browse the object.
It is often useful to start with a very rough subsetting, for example to data from a specific species. The `subset` function is useful for doing a standard R subsetting (the function also works on `data.frame`s).
```{r subset}
ah <- subset(ah, species == "Homo sapiens")
ah
```
We can use `query` to search the hub. The (possible) drawback to query is that it searches over different fields in the hub, so watch out with using a search term which is very non-specific. The query is a regular expression, which by default is case-insensitive. Here we locate all datasets on the **H3K4me3** histone modification (in H. sapiens because we selected this species above)
```{r query}
query(ah, "H3K4me3")
```
Another way of searching a hub is by using a browser. Notice how we assign the output of `display` to make sure that we can capture our selection in the browser
```{r display, eval=FALSE}
hist <- display(ah)
```
## SessionInfo
\scriptsize
```{r sessionInfo, echo=FALSE}
sessionInfo()
```
## References