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Showing content from https://github.com/whtns/chevreulProcess below:

cobriniklab/chevreulProcess: Processing of full-length scRNA-seq as SingleCellExperiments

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:

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.

clustered_sce <- sce_process(chevreul_sce,
    experiment_name = "sce_hu_trans",
    organism = "human"
)

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