In jgilis/satuRn_jgilis: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

knitr::opts_chunk$set(
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satuRn

satuRn is a highly performant and scalable method for performing differential transcript usage analyses.

Installation instructions

Get the development version of satuRn from GitHub with:

devtools::install_github("statOmics/satuRn")

The installation should only take a few seconds. The dependencies of the package are listed in the DESCRIPTION file of the package.

Issues and bug reports

Please use https://github.com/statOmics/satuRn/issues to submit issues, bug reports, and comments.

Usage

A minimal example of the different functions for modelling, testing and visualizing differential transcript usage is provided. See the online vignette for a more elaborate and reproducible example.

library("satuRn")

Provide a transcript expression matrix and corresponding colData and rowData

sumExp <- SummarizedExperiment::SummarizedExperiment(
    assays = list(counts = Tasic_counts_vignette),
    colData = Tasic_metadata_vignette,
    rowData = txInfo
)

# Specify design formula from colData
metadata(sumExp)$formula <- ~ 0 + as.factor(colData(sumExp)$group)

The fitDTU function is used to model transcript usage in different groups of samples or cells.

sumExp <- satuRn::fitDTU(
    object = sumExp,
    formula = ~0 + group, 
    parallel = FALSE,
    BPPARAM = BiocParallel::bpparam(),
    verbose = TRUE
)

Next we perform differential usage testing using with testDTU

sumExp <- satuRn::testDTU(object = sumExp, 
                          contrasts = L, 
                          plot = FALSE, 
                          sort = FALSE)

Finally, we may visualize the usage of select transcripts in select groups of interest with plotDTU

group1 <- rownames(colData(sumExp))[colData(sumExp)$group == "VISp.L5_IT_VISp_Hsd11b1_Endou"]
group2 <- rownames(colData(sumExp))[colData(sumExp)$group == "ALM.L5_IT_ALM_Tnc"]

plots <- satuRn::plotDTU(object = sumExp, 
                         contrast = "Contrast1", 
                         groups = list(group1, group2), 
                         coefficients = list(c(0, 0, 1), c(0, 1, 0)), 
                         summaryStat = "model", 
                         transcripts = c("ENSMUST00000081554", 
                                         "ENSMUST00000195963", 
                                         "ENSMUST00000132062"), 
                         genes = NULL, 
                         top.n = 6)

# Example plot from our publication:

Citation

Below is the citation output from using citation('satuRn') in R. Please run this yourself to check for any updates on how to cite satuRn.

print(citation("satuRn"), bibtex = TRUE)

Please note that the satuRn was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Code of Conduct

Please note that the satuRn project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

• Continuous code testing is possible thanks to GitHub actions through r BiocStyle::CRANpkg('usethis'), r BiocStyle::CRANpkg('remotes'), and r BiocStyle::CRANpkg('rcmdcheck') customized to use Bioconductor's docker containers and r BiocStyle::Biocpkg('BiocCheck').
• Code coverage assessment is possible thanks to codecov and r BiocStyle::CRANpkg('covr').
• The documentation website is automatically updated thanks to r BiocStyle::CRANpkg('pkgdown').
• The code is styled automatically thanks to r BiocStyle::CRANpkg('styler').
• The documentation is formatted thanks to r BiocStyle::CRANpkg('devtools') and r BiocStyle::CRANpkg('roxygen2').

For more details, check the dev directory.

This package was developed using r BiocStyle::Githubpkg('lcolladotor/biocthis').

jgilis/satuRn_jgilis documentation built on Jan. 21, 2021, 12:24 a.m.