---
title: "chevreulShiny"
author: 
  - name: Kevin Stachelek
    affiliation:
    - University of Southern California   
    email: kevin.stachelek@gmail.com
  - name: Bhavana Bhat
    affiliation:
    - University of Southern California   
    email: bbhat@usc.edu
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('chevreulShiny')`"
vignette: >
  %\VignetteIndexEntry{Preprocessing}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    dpi = 900,
    out.width = "100%",
    message = FALSE,
    warning = FALSE,
    crop = NULL)

## Related to https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
```


# Basics

## Install `chevreulShiny`

`R` is an open-source statistical environment which can be easily modified 
to enhance its functionality via packages. `r Biocpkg("chevreulShiny")` is a `R` 
package available via the [Bioconductor](http://bioconductor.org) repository 
for packages. `R` can be installed on any operating system from 
[CRAN](https://cran.r-project.org/) after which you can install 
`r Biocpkg("chevreulShiny")` by using the following commands in your `R` session:

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

BiocManager::install("chevreulShiny")

```

## Required knowledge

The `r Biocpkg("chevreulShiny")` package is designed for single-cell RNA sequencing
 data. The functions included within this package are derived from other 
 packages that have implemented the infrastructure needed for RNA-seq data
  processing and analysis. Packages that have been instrumental in the 
  development of `r Biocpkg("chevreulShiny")` include, 
  `Biocpkg("SummarizedExperiment")` and `Biocpkg("scater")`.

## Asking for help

`R` and `Bioconductor` have a steep learning curve so it is critical to 
learn where to ask for help. The 
[Bioconductor support site](https://support.bioconductor.org/) is the main 
resource for getting help: remember to use the `chevreulShiny` tag and check 
[the older posts](https://support.bioconductor.org/tag/chevreulShiny/). 


# Quick start to using `chevreulShiny`

The `chevreulShiny` package contains functions to preprocess, cluster, visualize, and 
perform other analyses on scRNA-seq data. It also contains a shiny app for easy 
visualization and analysis of scRNA data.

`chvereul` uses SingelCellExperiment (SCE) object type 
(from `r Biocpkg("SingleCellExperiment")`) 
to store expression and other metadata from single-cell experiments. 

This package features functions capable of:

* Performing Clustering at a range of resolutions and Dimensional 
reduction of Raw Sequencing Data.
* Visualizing scRNA data using different plotting functions.
* Integration of multiple datasets for consistent analyses. 
* Cell cycle state regression and labeling.

```{r, message=FALSE}

library("chevreulShiny")

# Load the data
data("small_example_dataset")
```

## Shiny app

chevreulShiny includes a shiny app for exploratory scRNA data analysis and 
visualization which can be accessed via

```{r "start", message=FALSE, eval = FALSE}

minimalChevreulApp(small_example_dataset)

```

Note: the SCE object must be pre-processed and integrated (if required) 
prior to building the shiny app.

The app is arranged into different sections each of which performs 
different function. More information about individual sections of the 
app is provided within the "shiny app" vignette. 

`R` session information.

```{r reproduce3, echo=FALSE}
## Session info
options(width = 120)
sessionInfo()
```