Package: muscat 1.27.2

Helena L. Crowell

muscat: Multi-sample multi-group scRNA-seq data analysis tools

`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

Authors:Helena L. Crowell [aut, cre], Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Jeroen Gilis [aut], Davide Risso [aut], Lieven Clement [aut], Mark D. Robinson [aut, fnd]

muscat_1.27.2.tar.gz
muscat_1.27.2.zip(r-4.7)muscat_1.27.2.zip(r-4.6)muscat_1.27.2.zip(r-4.5)
muscat_1.27.2.tgz(r-4.6-any)muscat_1.27.2.tgz(r-4.5-any)
muscat_1.27.2.tar.gz(r-4.7-any)muscat_1.27.2.tar.gz(r-4.6-any)
muscat_1.27.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
muscat/json (API)

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

Bug tracker:https://github.com/helenalc/muscat/issues

Datasets:

On BioConductor:muscat-1.27.0(bioc 3.24)muscat-1.26.0(bioc 3.23)

immunooncologydifferentialexpressionsequencingsinglecellsoftwarestatisticalmethodvisualization

11.62 score 231 stars 1 packages 932 scripts 1.1k downloads 4 mentions 15 exports 158 dependencies

Last updated from:85db626281. Checks:6 NOTE, 2 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE256
linux-devel-x86_64ERROR435
source / vignettesOK559
linux-release-x86_64NOTE472
macos-release-arm64NOTE387
macos-oldrel-arm64NOTE311
windows-develERROR450
windows-releaseNOTE412
windows-oldrelNOTE450
wasm-releaseOK204

Exports:aggregateDatabbhwcalcExprFreqsgBHmmDSpbDDpbDSpbFlattenpbHeatmappbMDSprepSCEprepSimresDSsimDatastagewise_DS_DD

Dependencies:abindaodassortheadbackportsbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsblmebootbroomCairocaToolscirclizecliclueclustercodetoolscolorspaceComplexHeatmapcorpcorcowplotcpp11crayonDelayedArrayDerivdigestdoBydoParalleldplyrdqrngedgeREnvStatsfANCOVAfarverFNNforeachforecastformatRfracdifffutile.loggerfutile.optionsgenericsGenomicRangesGetoptLongggbeeswarmggplot2ggrastrggrepelglmmTMBGlobalOptionsgluegplotsgridExtragtablegtoolshmsIRangesirlbaisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelifecyclelimmalme4lmerTestlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnortestnumDerivpbkrtestpheatmappillarpkgconfigplyrpngprettyunitsprogresspurrrR6raggrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppMLRcppProgressRdpackreformulasremaCorreshape2RhpcBLASctlrjsonrlangRSpectrarsvdRtsneS4ArraysS4VectorsS7sandwichScaledMatrixscalesscaterscuttleSeqinfoshapeSingleCellExperimentsitmosnowSparseArraystatmodstringistringrSummarizedExperimentsystemfontstextshapingtibbletidyrtidyselecttimeDateTMBurcautf8uwotvariancePartitionvctrsviporviridisviridisLitewithrXVectorzoo

Differential state analysis with muscat
Load packages | Introduction | What is DS analysis? | Starting point | Getting started | Data description | Loading the data | Preprocessing | Data preparation | Data overview | Cluster-sample sizes | Dimension reduction | Differential State (DS) analysis | Aggregation of single-cell to pseudobulk data | Pseudobulk-level MDS plot | Sample-level analysis: Pseudobulk methods | Cell-level analysis: Mixed models | Handling results | Results filtering & overview | Calculating expression frequencies | Formatting results | Visualizing results | Between-cluster concordance | DR colored by expression | Cell-level viz.: Violin plots | Sample-level viz.: Pseudobulk heatmaps | Session info | References
Rendered fromanalysis.Rmdusingknitr::rmarkdown

Last update: 2026-06-04
Started: 2020-02-04

Simulating complex design scRNA-seq data with muscat
Load packages | Data description | Simulation framework | prepSim: Preparing data for simulation | simData: Simulating complex designs | p_dd: Simulating differential distributions | rel_lfc: Simulating cluster-specific state changes | normalize & run dimension reduction | plotting | arrangement | Simulation a hierarchical cluster structure | p_type: Introducing type features | extract gene metadata & number of clusters | filter for type genes with high expression mean | sample 100 cells per cluster for plotting | filter for type & shared genes with high expression mean | Method benchmarking | Session info | References
Rendered fromsimulation.Rmdusingknitr::rmarkdown

Last update: 2026-06-04
Started: 2020-02-04

Differential detection analysis
Load packages | Introduction | Setup | Aggregation | Analysis | Handling and visualizing results | Stagewise anaysis | Comparison | Session info | References
Rendered fromdetection.Rmdusingknitr::rmarkdown

Last update: 2026-03-08
Started: 2024-10-07

Increasing power with bulk-based hypothesis weighing (bbhw)
Load packages | Methods | Creation of the evidence bins | P-value adjustment | Example usage | Generating input data | Differential State (DS) analysis | Generating fake bulk data | bbhw | Session info | References
Rendered frombbhw.Rmdusingknitr::rmarkdown

Last update: 2025-12-18
Started: 2025-02-12