Package: satuRn 1.15.0

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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satuRn.pdf |satuRn.html✨
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NEWS

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

Peer review:

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

Datasets:
  • 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.
  • 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.

On BioConductor:satuRn-1.15.0(bioc 3.21)satuRn-1.14.0(bioc 3.20)

regressionexperimentaldesigndifferentialexpressiongeneexpressionrnaseqsequencingsoftwaresinglecelltranscriptomicsmultiplecomparisonvisualization

6.95 score 20 stars 1 packages 74 scripts 506 downloads 9 exports 67 dependencies

Last updated 23 days agofrom:66563891f5. Checks:OK: 1 NOTE: 4 WARNING: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winNOTEOct 31 2024
R-4.4-macWARNINGOct 31 2024
R-4.3-winNOTEOct 31 2024
R-4.3-macWARNINGOct 31 2024

Exports:fitDTUgetCoefgetDFgetDfPosteriorgetDispersiongetModelplotDTUStatModeltestDTU

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocParallelbootclicodetoolscolorspacecpp11crayoncurlDelayedArrayfansifarverformatRfutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclelimmalocfdrmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslpbapplypillarpkgconfigR6RColorBrewerrlangS4ArraysS4VectorsscalessnowSparseArraystatmodSummarizedExperimentsystibbleUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

satuRn - vignette

Jeroen Gilis

Rendered fromVignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

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

Readme and manuals

Help Manual

Help pageTopics
fitDTUfitDTU fitDTU,SummarizedExperiment-method
Accessor functions for StatModel classgetCoef 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
StatModelStatModel
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 interesttestDTU