Package: satuRn 1.21.0

Jeroen Gilis

satuRn: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Authors:Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
satuRn/json (API)

# Install 'satuRn' in R:

install.packages('satuRn', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/statomics/saturn/issues

Datasets:

sumExp_example - A 'SummarizedExperiment' derived from our case study which builds on the dataset of Tasic et al. It contains the same cells as the data object used in the vignette (see '?Tasic_counts_vignette' for more information). In this SummarizedExperiment, we performed a filtering with 'filterByExpr' of edgeR with more stringent than default parameter settings (min.count = 100,min.total.count = 200, large.n = 50, min.prop = 0.9) to reduced the number of retained transcripts. We used this object to create an executable example in the help files of satuRn.
Tasic_counts_vignette - A 'Matrix' with transcript-level counts derived from our case study which builds on the dataset of Tasic et al. We used Salmon (V1.1.0) to quantify all L5IT cells (both for ALM and VISp tissue) from mice with a normal eye condition. From these cells, we randomly sampled 20 cells of each of the following cell types to use for this vignette; L5_IT_VISp_Hsd11b1_Endou, L5_IT_ALM_Tmem163_Dmrtb1 and L5_IT_ALM_Tnc. The data has already been leniently filtered with the 'filterByExpr' function of edgeR.
Tasic_metadata_vignette - Metadata associated with the expression matrix 'Tasic_counts_vignette'. See '?Tasic_counts_vignette' for more information on the dataset.

On BioConductor:satuRn-1.21.0(bioc 3.24)satuRn-1.20.0(bioc 3.23)

regression experimentaldesign differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

7.09 score 24 stars 1 packages 86 scripts 9 exports 47 dependencies

Last updated from:82b1889cbe. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	197
linux-devel-x86_64	NOTE	343
source / vignettes	OK	317
linux-release-x86_64	NOTE	321
macos-release-arm64	NOTE	244
macos-oldrel-arm64	NOTE	157
windows-devel	NOTE	3299
windows-release	NOTE	249
windows-oldrel	NOTE	235
wasm-release	OK	156

Exports:fitDTU getCoef getDF getDfPosterior getDispersion getModel plotDTU StatModel testDTU

Dependencies:abind BH Biobase BiocGenerics BiocParallel boot cli codetools cpp11 DelayedArray farver formatR futile.logger futile.options generics GenomicRanges ggplot2 glue gtable IRanges isoband labeling lambda.r lattice lifecycle limma locfdr Matrix MatrixGenerics matrixStats pbapply R6 RColorBrewer rlang S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray statmod SummarizedExperiment vctrs viridisLite withr XVector

satuRn - vignette

Rendered fromVignette.Rmdusingknitr::rmarkdown

Last update: 2022-07-14
Started: 2020-11-12

Help page	Topics
fitDTU	fitDTU fitDTU,SummarizedExperiment-method
Accessor functions for StatModel class	getCoef getCoef,StatModel-method getDF getDF,StatModel-method getDfPosterior getDfPosterior,StatModel-method getDispersion getDispersion,StatModel-method getModel getModel,StatModel-method statModelAccessors
Plot function to visualize differential transcript usage (DTU)	plotDTU
StatModel	StatModel
The StatModel class for satuRn	.StatModel StatModel-class
A `SummarizedExperiment` derived from our case study which builds on the dataset of Tasic et al. It contains the same cells as the data object used in the vignette (see `?Tasic_counts_vignette` for more information). In this SummarizedExperiment, we performed a filtering with `filterByExpr` of edgeR with more stringent than default parameter settings (min.count = 100,min.total.count = 200, large.n = 50, min.prop = 0.9) to reduced the number of retained transcripts. We used this object to create an executable example in the help files of satuRn.	sumExp_example
A `Matrix` with transcript-level counts derived from our case study which builds on the dataset of Tasic et al. We used Salmon (V1.1.0) to quantify all L5IT cells (both for ALM and VISp tissue) from mice with a normal eye condition. From these cells, we randomly sampled 20 cells of each of the following cell types to use for this vignette; L5_IT_VISp_Hsd11b1_Endou, L5_IT_ALM_Tmem163_Dmrtb1 and L5_IT_ALM_Tnc. The data has already been leniently filtered with the `filterByExpr` function of edgeR.	Tasic_counts_vignette
Metadata associated with the expression matrix `Tasic_counts_vignette`. See `?Tasic_counts_vignette` for more information on the dataset.	Tasic_metadata_vignette
Test function to obtain a top list of transcripts that are differentially used in the contrast of interest	testDTU

Package: satuRn 1.21.0

satuRn: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

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