---
title: "Using annotations to facilitate interactive exploration"
author: 
  - name: Kevin Rue-Albrecht
    affiliation:
    - University of Oxford
    email: kevin.rue-albrecht@imm.ox.ac.uk
output: 
  BiocStyle::html_document:
    self_contained: yes
    toc: true
    toc_float: true
    toc_depth: 2
    code_folding: show
date: "`r doc_date()`"
package: "`r pkg_ver('iSEEde')`"
vignette: >
  %\VignetteIndexEntry{Using annotations to facilitate interactive exploration}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL ## Related to https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
)
options(width = 100)
```

```{r, eval=!exists("SCREENSHOT"), include=FALSE}
SCREENSHOT <- function(x, ...) knitr::include_graphics(x)
```

```{r vignetteSetup, echo=FALSE, message=FALSE, warning = FALSE}
## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("RefManageR")

## Write bibliography information
bib <- c(
    R = citation(),
    BiocStyle = citation("BiocStyle")[1],
    knitr = citation("knitr")[1],
    RefManageR = citation("RefManageR")[1],
    rmarkdown = citation("rmarkdown")[1],
    sessioninfo = citation("sessioninfo")[1],
    testthat = citation("testthat")[1],
    iSEEde = citation("iSEEde")[1]
)
```

# Example data

In this example, we use the `?airway` data set. 

```{r, message=FALSE, warning=FALSE}
library("airway")
data("airway")
```

# Annotating data

This section demonstrates one of many possible workflows for adding annotations
to the data set. Those annotations are meant to make it easier for users to
identify genes of interest, e.g. by displaying both gene symbols and ENSEMBL 
gene identifiers as tooltips in the interactive browser.

First, we make a copy of the Ensembl identifiers -- currently stored in the 
`rownames()` -- to a column in the `rowData()` component.

```{r}
rowData(airway)[["ENSEMBL"]] <- rownames(airway)
```

Then, we use the `r BiocStyle::Biocpkg("org.Hs.eg.db")` package to map the 
Ensembl identifiers to gene symbols. We store those gene symbols as an
additional column of the `rowData()` component.

```{r, message=FALSE, warning=FALSE}
library("org.Hs.eg.db")
rowData(airway)[["SYMBOL"]] <- mapIds(
    org.Hs.eg.db, rownames(airway),
    "SYMBOL", "ENSEMBL"
)
```

Next, we use the `uniquifyFeatureNames()` function of the 
`r BiocStyle::Biocpkg("scuttle")` package to replace the `rownames()` by a
unique identifier that is generated as follows:

- The gene symbol if it is unique.
- A concatenate of the gene symbol and Ensembl gene identifier if the gene
  symbol is not unique.
- The Ensembl identifier if the gene symbol is not available.

```{r, message=FALSE, warning=FALSE}
library("scuttle")
rownames(airway) <- uniquifyFeatureNames(
    ID = rowData(airway)[["ENSEMBL"]],
    names = rowData(airway)[["SYMBOL"]]
)
airway
```

# Differential expression

To generate some example results, we first use `edgeR::filterByExpr()` to remove
genes whose counts are too low to support a rigorous differential expression
analysis. Then we run a standard Limma-Voom analysis using `edgeR::voomLmFit()`,
`limma::makeContrasts()`, and `limma::eBayes()`; alternatively, we could have
used `limma::treat()` instead of `limma::eBayes()`.

The linear model includes the `dex` and `cell` covariates, indicating the
treatment conditions and cell types, respectively. Here, we are interested in 
differences between treatments, adjusted by cell type, and define this
comparison as the `dextrt - dexuntrt` contrast.

The final differential expression results are fetched using `limma::topTable()`.

```{r, message=FALSE, warning=FALSE}
library("edgeR")

design <- model.matrix(~ 0 + dex + cell, data = colData(airway))

keep <- filterByExpr(airway, design)
fit <- voomLmFit(airway[keep, ], design, plot = FALSE)
contr <- makeContrasts("dextrt - dexuntrt", levels = design)
fit <- contrasts.fit(fit, contr)
fit <- eBayes(fit)
res_limma <- topTable(fit, sort.by = "P", n = Inf)
head(res_limma)
```

Then, we embed this set of differential expression results in the `airway`
object using the `embedContrastResults()` method and we use the function `contrastResults()` to display the contrast results stored in the `airway` object.

```{r}
library(iSEEde)
airway <- embedContrastResults(res_limma, airway,
    name = "dextrt - dexuntrt",
    class = "limma"
)
contrastResults(airway)
contrastResults(airway, "dextrt - dexuntrt")
```

# Live app

In this example, we use `iSEE::panelDefaults()` to specify `rowData()` fields to
show in the tooltip that is displayed when hovering a data point.

The application is then configured to display the volcano plot and MA plot for
the same contrast.

Finally, the configured app is launched.

```{r, message=FALSE, warning=FALSE}
library(iSEE)

panelDefaults(
    TooltipRowData = c("SYMBOL", "ENSEMBL")
)

app <- iSEE(airway, initial = list(
    VolcanoPlot(ContrastName = "dextrt - dexuntrt", PanelWidth = 6L),
    MAPlot(ContrastName = "dextrt - dexuntrt", PanelWidth = 6L)
))

if (interactive()) {
    shiny::runApp(app)
}
```

```{r, echo=FALSE, out.width="100%"}
SCREENSHOT("screenshots/using_annotations.png", delay = 20)
```

# Reproducibility

The `r Biocpkg("iSEEde")` package `r Citep(bib[["iSEEde"]])` was made possible
thanks to:

* R `r Citep(bib[["R"]])`
* `r Biocpkg("BiocStyle")` `r Citep(bib[["BiocStyle"]])`
* `r CRANpkg("knitr")` `r Citep(bib[["knitr"]])`
* `r CRANpkg("RefManageR")` `r Citep(bib[["RefManageR"]])`
* `r CRANpkg("rmarkdown")` `r Citep(bib[["rmarkdown"]])`
* `r CRANpkg("sessioninfo")` `r Citep(bib[["sessioninfo"]])`
* `r CRANpkg("testthat")` `r Citep(bib[["testthat"]])`

This package was developed using `r BiocStyle::Biocpkg("biocthis")`.


Code for creating the vignette

```{r createVignette, eval=FALSE}
## Create the vignette
library("rmarkdown")
system.time(render("annotations.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("annotations.Rmd", tangle = TRUE)
```

Date the vignette was generated.

```{r reproduce1, echo=FALSE}
## Date the vignette was generated
Sys.time()
```

Wallclock time spent generating the vignette.

```{r reproduce2, echo=FALSE}
## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)
```

`R` session information.

```{r reproduce3, echo=FALSE}
## Session info
library("sessioninfo")
options(width = 120)
session_info()
```



# Bibliography

This vignette was generated using `r Biocpkg("BiocStyle")` 
`r Citep(bib[["BiocStyle"]])` with `r CRANpkg("knitr")` 
`r Citep(bib[["knitr"]])` and `r CRANpkg("rmarkdown")` 
`r Citep(bib[["rmarkdown"]])` running behind the scenes.

Citations made with `r CRANpkg("RefManageR")` `r Citep(bib[["RefManageR"]])`.

```{r vignetteBiblio, results = "asis", echo = FALSE, warning = FALSE, message = FALSE}
## Print bibliography
PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))
```

<!-- Links -->

[scheme-wikipedia]: https://en.wikipedia.org/wiki/Uniform_Resource_Identifier#Syntax
[iana-uri]: https://www.iana.org/assignments/uri-schemes/uri-schemes.xhtml
