---
title: "chevreulProcess"
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('chevreulProcess')`"
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 `chevreulProcess`

`R` is an open-source statistical environment which can be easily modified 
to enhance its functionality via packages. `r Biocpkg("chevreulProcess")` 
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("chevreulProcess")` by using the following commands in your `R` 
session:

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

BiocManager::install("chevreulProcess")

```

## Required knowledge

The `r Biocpkg("chevreulProcess")` 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("chevreulProcess")` 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 `chevreulProcess` tag and check 
[the older posts](https://support.bioconductor.org/tag/chevreulProcess/). 

# Quick start to using `chevreulProcess`

The `chevreulProcess` 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("chevreulProcess")

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

`R` session information.

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

