---
title: "omXplore: a versatile series of Shiny apps to explore 'omics' data"
author: 
- name: Samuel Wieczorek
- name: Thomas Burger
package: omXplore
abstract: >
    The package omXplore (standing for "Omics Explore") is a R package which 
    provides functions to vizualize omics data experiments. It deals with 
    common data formats in Bioconductor such as MSnSet, MultiAssayEperiments, 
    QFeatures or even simple lists.
date: "`r Sys.Date()`"
output:
    BiocStyle::html_document:
        toc_float: true
vignette: >
    %\VignetteIndexEntry{omXplore}
    %%\VignetteKeywords{Software}
    %\VignetteEncoding{UTF-8}
    %\VignetteEngine{knitr::rmarkdown}
---

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```


# Introduction

The `omXplore` package offers a series of built-in plots dedicated to the 
visualization and the analysis of *omics (genomic, transcriptomics, proteomics) 
data. As for several R packages available in `Biocondcutor` for exploring 
omics-like datasets, `omXplore` is based on Shiny to make those plots easily 
available in a web application. Four popular Bioconductor data objects are 
currently supported: `SummarizedExperiment`, `MultiAssayExperiment`, `MSnset` 
and `QFeatures.`It is also possible to use `data.frame` or `matrix` (or lists of)
which contains quantitative data tables (rows for features and columns for
samples).

All these formats are automatically converted into an internal S4 class which 
is an enriched version of the `MultiAssayexperiment` class. This process is
invisible to the end-user.


The package `omXplore` was created to be versatile, reusable and scalable. It 
differs from similar R packages in two main points:

- (versatile) The main Shiny module in `omXplore` is a hub which gives access 
to the individual plots. The plots are automatically updated w.r.t the selected 
dataset (in case of data which contains several ExperimentData like the 
classes `MultiAssayExperiment` or `QFeatures`).

- (scalable) with less effort, it is easy integrate external plots (written as 
Shiny modules) in the main GUI of `omXplore`.

- (reusable) Each plot (a Shiny module) can be run alone or integrated as a 
complementary tool in third party Shiny apps. As an example, it is well suited 
for the package 
[Prostar](https://www.bioconductor.org/packages/release/bioc/html/Prostar.html)
in which it is used.


# Features

`omXplore` provides a graphical user interface using the `Shiny` and 
the `highcharter` packages for the following visualizations:

- Connected Components of graph-type data (e.g. in proteomics datasets, graphs 
of peptide-protein relationship),
- Principal Component Analysis (PCA),
- Histograms to analyze the quantitative data based on cell metadata 
(eg missing values, imputed values, ...), 
- Intensity plots (boxplot and violinplot) of quantitative data. There is also 
a tool to select and view (by superimposition) the evolution of intensity over 
samples for a given set of entities,
- Density plot
- Variance distribution plot
- Heatmap
- Correlation plot


For developers or users who wants to enhance their application, additional 
features include:

- As each plot is a Shiny module, it can be launched in a standalone mode 
(from a R console) or it can be easily integrated into any third party Shiny app
- Internal convert function from a large variety of `Bioconductor` objects,
- A easy way to add user-defined plots (written as Shiny modules) in the main 
GUI of `omXplore` 



# Installation


To install this package, start R (version "4.3") and enter:

```
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("omXplore")
```

This will also install dependencies.

It is also possible to install `omXplore` from Github:

```
library(devtools)
install_github('prostarproteomics/omXplore')

```


Then, load the package into R environment:

```{r env}
library("omXplore")
```


# Enriching native MultiAssayExperiment

The internal data struture used in `omXplore` is based on the class [MultiAssayExperiments](https://www.bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html) which is enriched with specific slots needed to create built-in plots.
The plots display statistical information about data contained in the SummarizedExperiment
slots of a dataset.

Some items are added to the metadata of the instance of
MultiAssayExperiment and to each of the instances of SummarizedExperiment.

**Additional info in MultiAssayExperiment**

Currently, there is no custom slot in the metadata of the mae contains
the following additional items (in the slot names `other`):

```{r meta_mae, echo = TRUE}
data(vdata)
MultiAssayExperiment::metadata(vdata)
```

**Additional info in SummarizedExperiment**

The metadata of each SummarizedExperiment dataset contains the following
items: 

-   **proteinID**: the name of the column which contains xxx

-   **colID**: the name of the column which serves as unique index

-   **cc**: the list of Connected Components, based on the adjacency
    matrix (if exists)

-   **type**: the type of data contained in the current Experiment (e.g. peptide, protein, ...)

-   **pkg_version**: the name and version number which has been used to
    create the current Experiment.


```{r meta_se, echo = TRUE}
data(vdata)
MultiAssayExperiment::metadata(vdata[[1]])
```

The **adjacencyMatrix** (when exists) is stored as a DataFrame in the
rowData() of a SummarizedExperiment itemµ.


All modules are self-contained in the sense that it is not necessary to manipulate datasets to view the plots. The information described above are given only to discover the slots used if the user wants to enrich its dataset before using omXplore.


# Using omXplore

The package `omXplore` offers a collection of standard plots written as Shiny modules.
The main app is a Shiny module itself which displays the plots of each
module.

This section describes how to view built-in plots and the main app of `omXplore`.

## Individual built-in plots

The list of plots available in the current R session via omXplore can be obtained with: 

```{r listShinyApps}
listPlotModules()
```

By default, this function lists the built-in modules and the external modules compliant with omXplore.

Each of these functions is a Shiny app implemented as a module and can be launched in a standalone mode or embedded in another shiny app (as it is the case with the main UI of `omXplore` or inserted in a third party Shiny app).


Most of these functions analyse the data contained in an Experiment of the dataset (an instance of the class `SummarizedExperiment`). For a sake of simplicity, they all have the same two parameters: (1) the dataset in any (compatible) format (See the help page of the plot functions for details) and (2) the indice of the assay to analyse (See [MultiAssayExperiment](https://www.bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html)). 

Internally, each function builds the enriched instance of MAE used inside omXplore then show the plot for the assay which has been specified in parameters.


```{r ex_modules, eval=FALSE}
data(sub_R25)
app <- omXplore_density(sub_R25, 1)
shiny::runApp(app)
```

Note: this code to run a shiny app follows the recommendations of Bioconductor on [Running Shiny apps](https://contributions.bioconductor.org/shiny.html#running-app).

```{r omXplore_density_png, results='markup', fig.cap="Plot generated by the module omXplore_density()", echo=FALSE, out.width='100%', fig.align='center', fig.wide = TRUE}
knitr::include_graphics("./figs/omXplore_density.png", error = FALSE)
```



## Main UI

As it is 9described in the previous section, omXplore have several built-in plots. And it may be fastidious to launch each plot function one after one to completely analyze a dataset.

For that purpose, `omXplore` has another shiny app, called `view_dataset()` which acts as a hub for plots to facilitate the analyse of the different assays in a dataset. It is launched as follows:

```{r view_dataset_modal, eval=FALSE}
data(sub_R25)
app <- view_dataset(sub_R25)
shiny::runApp(app)
```

The resulting UI is the following:

```{r example_view_dataset_nomodal_png, results='markup', fig.cap="`omXplore` interactive interface with modal.", echo=FALSE, out.width='100%', fig.align='center', fig.wide = TRUE}
knitr::include_graphics("./figs/example_view_dataset_nomodal.png", error = FALSE)
```

The interface is divided in three parts.

**(A) Choosing the assay** 

A widget let the user select one of the experiments contained in the dataset.

**(B) Select which plot to display**

A series of clickable vignettes which represent the different plots available. When the user clicks on a vignette, the corresponding plot is displayed (See are C).

**(C) Viewing the plots**

The plots are displayed in the same window as the UI (below the vignettes) or in a modal window, depending of the option used to launch the Shinyp app (See `?view_dataset`).


```{r view_dataset_nomodal_png, results='markup', fig.cap="`omXplore` interactive interface with modal.", echo=FALSE, out.width='100%', fig.align='center', fig.wide = TRUE}
knitr::include_graphics("./figs/view_dataset_modal.png", error = FALSE)
```

When a plot is displayed, it shows the data corresponding to the dataset
selected in the widget (of the left side). If this dataset is changed, the plot is 
automatically updated with the data of the new dataset.

# Session information

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