README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
    eval = FALSE,
    collapse = TRUE,
    comment = "#>",
    dpi = 900,
    fig.path = "man/figures/README-",
    out.width = "100%"
)
```


# chevreulProcess

This package includes a set of Shiny apps for exploring single cell RNA datasets processed as a SingleCellExperiment

A demo using a human gene transcript dataset from Shayler et al. (link) is available <a href="https://docker.saban.chla.usc.edu/cobrinik/app/seuratApp/" target="_blank" rel="noopener noreferrer">here</a>

There are also convenient functions for:

-   Clustering and Dimensional Reduction of Raw Sequencing Data.
-   Integration and Label Transfer
-   Louvain Clustering at a Range of Resolutions
-   Cell cycle state regression and labeling

> [!WARNING]
> chevreulProcess was designed for full-length smart-seq based single cell data. Default settings may not be appropriate for droplet (10x) data, though most can be adjusted. Keep in mind best practices regarding normalization, dimensional reduction, etc. when using. 

## Installation

You can install the released version of chevreulProcess from <a href="https://github.com/cobriniklab/chevreulProcess" target="_blank" rel="noopener noreferrer">github</a> with:

### Install locally and run in three steps:

You can install chevreulProcess locally using the following steps:

```{r, eval=FALSE}
install.packages("devtools")
devtools::install_github("cobriniklab/chevreulProcess")
chevreulProcess::create_project_db()
```

You can also customize the location of the app using these steps:

```{r, eval=FALSE}
devtools::install_github("cobriniklab/chevreulProcess")
chevreulProcess::create_project_db(destdir = "/your/path/to/app")
```


## Getting Started

First, load chevreulProcess and all other packages required 

```{r}
library(chevreulProcess)
library(SingleCellExperiment)
library(tidyverse)
library(ggraph)
```

## TLDR

chevreulProcess provides a single command to:

-   construct a SingleCellExperiment object

-   filter genes by minimum expression and ubiquity

-   normalize and scale expression by any of several methods packaged in SingleCellExperiment

## Run clustering on a single object

By default clustering will be run at ten different resolutions between 0.2 and 2.0. Any resolution can be specified by providing the resolution argument as a numeric vector.

```{r, cache = FALSE, eval = FALSE}
clustered_sce <- sce_process(chevreul_sce,
    experiment_name = "sce_hu_trans",
    organism = "human"
)
```