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 here
There are also convenient functions for:
[!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.
You can install the released version of chevreulProcess from github with:
Install locally and run in three steps:You can install chevreulProcess locally using the following steps:
install.packages("devtools") devtools::install_github("cobriniklab/chevreulProcess") chevreulProcess::create_project_db()
You can also customize the location of the app using these steps:
devtools::install_github("cobriniklab/chevreulProcess") chevreulProcess::create_project_db(destdir = "/your/path/to/app")
First, load chevreulProcess and all other packages required
library(chevreulProcess) library(SingleCellExperiment) library(tidyverse) library(ggraph)
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
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.
clustered_sce <- sce_process(chevreul_sce, experiment_name = "sce_hu_trans", organism = "human" )
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