---
title: "RNA-seq data with different levels of gDNA contamination"
author:
- name: Robert Castelo
  affiliation:
  - &id Dept. of Medicine and Life Sciences,
    Universitat Pompeu Fabra, Barcelona, Spain
  email: robert.castelo@upf.edu
package: "`r pkg_ver('gDNAinRNAseqData')`"
abstract: >
  The gDNAinRNAseqData package provides access through the ExperimentHub,
  to RNA-seq BAM files containing different levels of genomic DNA (gDNA)
  contamination. This vignette illustrates how to download them.
vignette: >
  %\VignetteIndexEntry{RNA-seq data with different levels of gDNA contamination}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
    toc: true
    toc_float: true
    number_sections: true
---

```{css style settings, echo = FALSE}
blockquote {
    padding: 10px 20px;
    margin: 0 0 20px;
    font-size: 14px;
    border-left: 5px solid #eee;
}
```

```{r setup, echo=FALSE}
options(width=80)
```

# Retrieval of the gDNA contaminated RNA-seq data by Li et al. (2022)

Here we show how to download a subset of the RNA-seq data published in:

> Li, X., Zhang, P., and Yu. Y. Gene expressed at low levels raise false
> discovery rates in RNA samples contaminated with genomic DNA. _BMC Genomics_,
> 23:554, 2022. https://doi.org/10.1186/s12864-022-08785-1

The subset of the data available through this package are BAM files containing
about 100,000 alignments, sampled uniformly at random from complete BAM files.
These complete BAM files were obtained by aligning the RNA-seq reads sequenced
from total RNA libraries mixed with different concentrations of gDNA,
concretely 0% (no contamination), 1% and 10%; see Fig. 2 from Li et al. (2022).
The original RNA-seq data is publicly available at
https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA007961 and you can find the
pipeline to generate this subset of the data in the file
`gDNAinRNAseqData/inst/scripts/make-data_LiYu22subsetBAMfiles.R` stored in this
package.

To download these subsetted BAM files, and the corresponding index (.bai) files,
we load this package and call the function `LiYu22subsetBAMfiles()`:

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

bamfiles <- LiYu22subsetBAMfiles()
bamfiles
```

The previous function call can take a `path` argument to specify the path in
the filesystem where we would like to store the downloaded BAM files, which
by default is a temporary path from the current R session; consult the help
page of `LiYu22subsetBAMfiles()` for full details.

We can also retrieve the gDNA concentrations associated to each BAM file with
the following function call:

```{r}
pdat <- LiYu22phenoData(bamfiles)
pdat
```

# Session information

```{r session_info, cache=FALSE}
sessionInfo()
```