README.md

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

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)
#> 
#> jgilis (2021). _Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell
#> RNA-sequencing Applications_. doi: 10.18129/B9.bioc.satuRn (URL:
#> https://doi.org/10.18129/B9.bioc.satuRn), https://github.com/jgilis/satuRn - R package version 0.99.0,
#> <URL: http://www.bioconductor.org/packages/satuRn>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications},
#>     author = {{jgilis}},
#>     year = {2021},
#>     url = {http://www.bioconductor.org/packages/satuRn},
#>     note = {https://github.com/jgilis/satuRn - R package version 0.99.0},
#>     doi = {10.18129/B9.bioc.satuRn},
#>   }
#> 
#> jgilis (2020). "Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell
#> RNA-sequencing Applications." _bioRxiv_. doi: 10.1101/TODO (URL: https://doi.org/10.1101/TODO), <URL:
#> https://www.biorxiv.org/content/10.1101/TODO>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications},
#>     author = {{jgilis}},
#>     year = {2020},
#>     journal = {bioRxiv},
#>     doi = {10.1101/TODO},
#>     url = {https://www.biorxiv.org/content/10.1101/TODO},
#>   }

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 usethis, remotes, and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
• Code coverage assessment is possible thanks to codecov and covr.
• The documentation website is automatically updated thanks to pkgdown.
• The code is styled automatically thanks to styler.
• The documentation is formatted thanks to devtools and roxygen2.

For more details, check the dev directory.

This package was developed using biocthis.

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