satuRn is a highly performant and scalable method for performing differential transcript usage analyses.
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.
Please use https://github.com/statOmics/satuRn/issues to submit issues, bug reports, and comments.
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:
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.
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.
- 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.