This package enables plotting of single cell RNA datasets processed as SingleCellExperiments
A demo with a developing human retina scRNA-seq dataset from Shayler et al. is available here
This package includes a set of Shiny apps for interactive exploration of single cell RNA sequencing (scRNA-seq) datasets processed as SingleCellExperiments
chevreulPlot includes tools for:
chevreulPlot depends on a minimum R version 4.4. chevreulPlot loads the Bioconductor package
which enables standardized processing of SingleCellExperiments.
chevreulPlot depends on a minimum R version >=4.4 Get the latest
stable R release from CRAN. Then install
chevreulPlot and its dependencies using the following code:
install.packages("BiocManager")
BiocManager::install("chevreulPlot")
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.
data("small_example_dataset")
clustered_sce <- sce_clustering_workflow(small_example_dataset,
experiment_name = "sce_hu_trans",
organism = "human"
)
minimalChevreulApp(small_example_dataset)
When installing an R package like chevreulPlot with many dependencies, conflicts with existing installations can arise. This is a common issue in R package management. Here are some strategies to address this problem:
Consider renv for dependency management. This tool creates isolated environments for each project, ensuring that package versions don’t conflict across different projects.
Use the conflicted Package The conflicted package provides an alternative conflict resolution strategy. It makes every conflict an error, forcing you to choose which function to use
When installing R packages on slow internet connections, several issues can arise, particularly with larger packages or when using functions like remotes::install_github(). Here are some strategies to address bandwidth-related problems:
Set a longer timeout for downloads: options(timeout = 9999999)
Specify the download method: options(download.file.method = "libcurl")
For transcript-level analysis, users must incorporate transcript-level data into the SingleCellExperiment object as an alternative experiment before initiating the chevreulProcess processing pipeline. This step is crucial for enabling detailed exploration at the transcript level.
Transcripts may be quantified using any of several available methods, including alignment-free methods best used with well-annotated transcriptomes (Salmon, Kallisto), alignment-based methods best used to detect novel isoforms (StringTie2), or long-read methods for use with long-read sequencing data (IsoQuant).
The sce_integrate() function in chevreulProcess implements integration
(batch correction) of scRNA-seq datasets by using the
batchelor
package.
It accepts a list of SingleCellExperiment objects as input for
integration and stores the corresponding batch information in a metadata
field named ‘batch’. By default, it employs batchelor’s
correctExperiments function to preserve pre-existing data structures
and metadata from input SingleCellExperiment objects within the
integrated output.
Recommended minimum hardware requirements for running chevreulPlot are as follows:
It’s important to note that these requirements can vary based on the size and complexity of your dataset. As the number of cells increases, so do the hardware requirements. For instance: A dataset with around 8,000 cells can be analyzed with 8 GB of RAM. For larger datasets or more complex analyses, 64-128 GB of RAM can be beneficial.
To learn more about the usage of Bioconductor tools for single-cell RNA-seq analysis. Consult the book Orchestrating Single-Cell Analysis with Bioconductor. The book walks through common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). This book will show you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data