---
title: "Accessing RLHub Data"
author:
  - name: "Henry Miller"
    affiliation: 
      - Alex Bishop Laboratory, UT Health San Antonio
      - Bioinformatics Research Network
package: RLHub
date: "`r doc_date()`"
output: 
  BiocStyle::html_document:
    toc_float: true
  BiocStyle::pdf_document: default
vignette: >
  %\VignetteIndexEntry{Accessing RLHub Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8} 
---

# Introduction

<img src="https://rlbase-data.s3.amazonaws.com/misc/assets/whitebgRLHub+Logo.png" align="right" alt="logo" width="240" style = "border: none; float: right;">

RLHub ("R-Loop Hub") provides processed data sets for the RLSuite toolchain. It is an `ExperimentHub` package containing annotations of R-Loop consensus regions, genomic features directly relevant to R-loops, such as R-loop-forming sequences (RLFS), G-or-C skew regions, and other data of relevance to RLSuite.

All data were generated via the protocol in the [RLBase-data repository](https://github.com/Bishop-Laboratory/RLBase-data).

# Installation

RLHub can be installed from Bioconductor via the following command:

```{.r}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("RLHub")
```

RLHub may also be installed from GitHub:

```{.r}
remotes::install_github("Bishop-Laboratory/RLHub")
```

# Accessing RLHub Data

```{r}
library(RLHub)
```

Data can be conveniently accessed through `ExperimentHub` functions or with the built-in accessors available through `RLHub`.

A summary of the data can also be found by running the following:

```{.r}
?`RLHub-package`
```

The full manifest of the available data is found here:

```{r rlhub-types}
DT::datatable(
    read.csv(system.file("extdata", "metadata.csv", package = "RLHub")),
    options = list(
        scrollX=TRUE,
        pageLength = 5
    )
)
```

The **Tags** column list the function names used to access each data set. This method of access is detailed below.

## Built-in functions

In the below example, we show how one can access data using convenient built-in functions. 

```{r get-rlhub, message=FALSE}
# Access the R-loop binding proteins (RLBPs) data set
rlbps <- RLHub::rlbps()
DT::datatable(rlbps)
```

The data access function name is simply the value in **Tags** corresponding to the entry for that data set in the `metadata.csv` table. In this example,"rlbps" is the tag corresponding to entry \#5: "R-loop-binding proteins discovered from mass-spec studies." Therefore, the function to access this data is simply `RLHub::rlbps()`.

For examples of all accessors, please run the following:

```{.r}
?`RLHub-package`
```

## ExperimentHub objects

```{r, message=FALSE}
library(ExperimentHub)
```

In this example, we show how to access RLHub data using the ExperimentHub object.

```{r, message=FALSE}
eh <- ExperimentHub()
rlhub <- query(eh, "RLHub")
rlhub
```

If we want to obtain the R-loop-binding proteins, for example, we can do so with corresponding ExperimentHub ID.

```{r, message=FALSE}
rlbps <- rlhub[["EH6797"]]
DT::datatable(rlbps)
```

Finally, all package resources may be loaded as a list using `loadResources()`.

```{.r}
rlhublst <- loadResources(rlhub, package = "RLHub")
names(rlhublst) <- listResources(rlhub, package = "RLHub")
```

# Session info

```{r sessionInfo}
sessionInfo()
```