---
title: "Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data"
author: "Nan Xiao <<https://nanx.me>><br>
         Gao Wang <<https://home.uchicago.edu/gaow/>><br>
         Lei Sun <<sunl@uchicago.edu>>"
date: "`r Sys.Date()`"
bibliography: grex.bib
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    highlight: "textmate"
    css: "custom.css"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data}
---

# Introduction

The Genotype-Tissue Expression (GTEx) project [@gtex] aims at measuring human
tissue-specific gene expression levels. With the collected data,
we will be able to explore the landscape of gene expression, gene regulation,
and their deep connections with genetic variations.

Raw GTEx data contains expression measurements from various types
of elements (such as genes, pseudogenes, noncoding DNA sequences)
covering the whole genome. For some analysis, it might be desirable
to only keep a subset of the data, for example, data from protein coding genes.
In such cases, mapping the original Ensembl gene IDs to Entrez gene IDs or
HGNC symbols become an essential step in the analysis pipeline.

`grex` offers a **minimal dependency** solution to do such ID mappings.
Currently, an Ensembl ID from GTEx can be mapped to its Entrez gene ID,
HGNC gene symbol, and UniProt ID, with basic annotation information
such as HGNC gene name, cytogenetic location, and gene type.
We also limit our scope on the Ensembl IDs appeared in the gene read count data.
Ensembl IDs from transcript data will be considered in future versions.

# Mapping Table

To facilitate such ID conversion tasks, the `grex` package has a built-in
mapping table derived from the well-known annotation data package
`org.Hs.eg.db` [@hsdb]. The mapping data we used has integrated mapping
information from Ensembl and NCBI, to maximize the possibility of finding
a matched Entrez ID. The R script for creating the mapping table is located 
[here](https://github.com/nanxstats/grex/blob/master/data-raw/mapping-table-generator.R).

Not surprisingly, when creating such a table, there were hundreds of cases
where a single Ensembl ID can be mapped to multiple Entrez gene IDs.
To create a one-to-one mapping, we took a simple approach: we just removed
the duplicated Entrez IDs and only kept the first we encountered in the
original database. Therefore, there might be cases where the mapping is
not 100% accurate. If you have such doubts for particular results,
please try searching the original ID on the Ensembl website and see if we
got a correct mapped ID.

# Code Example

As an example, we use the Ensembl IDs from GTEx V7 gene count data
and select 100 IDs:

```{r,echo=FALSE}
options(width = 90)
```

```{r}
library("grex")
data("gtexv7")
id <- gtexv7[101:200]
df <- grex(id)
tail(df)
```

The elements which cannot be mapped accurately will be `NA`.

Genes with a mapped Entrez ID:

```{r}
filtered_genes <-
  df[
    !is.na(df$"entrez_id"),
    c("ensembl_id", "entrez_id", "hgnc_symbol", "gene_biotype")
  ]
head(filtered_genes)
```

If you want to start from the raw GENCODE gene IDs provided by GTEx
(e.g. `ENSG00000227232.4`), the function `cleanid()` can help you
remove the `.version` part in them, to produce Ensembl IDs.

# What's Next?

Conventionally, the next step is removing (or imputing) the genes
with `NA` IDs, and then select the genes to keep.
Notably, as was observed in the complete gene read count data,
in about 100 cases, multiple Ensembl IDs can be mapped to one
single Entrez ID. Post-processing steps may also be needed for such genes.

# Acknowledgements

We thank members of the [Stephens lab](http://stephenslab.uchicago.edu)
(Kushal K Dey, Michael Turchin) for their valuable suggestions
and helpful discussions on this problem.

# References
