Package: sccomp 2.5.0

Stefano Mangiola

sccomp: Differential Composition and Variability Analysis for Single-Cell Data

Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications.

Authors:Stefano Mangiola [aut, cre], Alexandra J. Roth-Schulze [aut], Marie Trussart [aut], Enrique Zozaya-Valdés [aut], Mengyao Ma [aut], Zijie Gao [aut], Alan F. Rubin [aut], Terence P. Speed [aut], Heejung Shim [aut], Anthony T. Papenfuss [aut]

sccomp_2.5.0.tar.gz
sccomp_2.5.0.zip(r-4.7-any)sccomp_2.5.0.zip(r-4.6-any)sccomp_2.5.0.zip(r-4.5-any)
sccomp_2.5.0.tgz(r-4.6-any)sccomp_2.5.0.tgz(r-4.5-any)
sccomp_2.5.0.tar.gz(r-4.7-any)sccomp_2.5.0.tar.gz(r-4.6-any)
sccomp_2.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
sccomp/json (API)

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

Bug tracker:https://github.com/mangiolalaboratory/sccomp/issues

Pkgdown/docs site:https://mangiolalaboratory.github.io

Datasets:

On BioConductor:sccomp-2.5.0(bioc 3.24)sccomp-2.4.0(bioc 3.23)

bayesianregressiondifferentialexpressionsinglecellmetagenomicsflowcytometryspatialbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation

9.54 score 134 stars 217 scripts 614 downloads 20 exports 75 dependencies

Last updated from:9925730441. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksERROR211
linux-develNOTE401
source / vignettesOK292
linux-releaseNOTE476
macos-releaseNOTE283
macos-oldrelNOTE280
windows-develNOTE274
windows-releaseNOTE266
windows-oldrelNOTE259
wasm-releaseOK178

Exports:clear_draw_filesclear_stan_model_cacheplot_1D_intervalsplot_2D_intervalssccomp_boxplotsccomp_calculate_residualssccomp_estimatesccomp_plot_intervals_1Dsccomp_plot_intervals_2Dsccomp_predictsccomp_proportional_fold_changesccomp_remove_outlierssccomp_remove_unwanted_effectssccomp_remove_unwanted_variationsccomp_replicatesccomp_scatterplotsccomp_stan_models_cache_dirsccomp_testsccomp_themesimulate_data

Dependencies:abindbackportsBiobaseBiocGenericsbitbit64bootcallrcheckmateclicliprcpp11crayonDelayedArraydistributionaldplyrfansifarverforcatsfsgenericsGenomicRangesggplot2ggrepelggsidegluegtablehmsinstantiateIRangesisobandlabelinglatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsnumDerivotelpatchworkpillarpkgconfigposteriorprettyunitsprocessxprogresspspurrrR6RColorBrewerRcppreadrrlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentSparseArraystringistringrSummarizedExperimenttensorAtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrXVector

Smooth (spline) terms in sccomp
Why smooth terms? | What is supported | Continuous covariate | 1. Fit ~ type + s(pseudotime, k = 5) | 2. Visualise the curve | 3. Let's use a smaller basis (k = 3) | 4. Under the hood: What s() actually decomposes into | Hierarchical smooths: per-group curves with shared structure | 1. Fit ~ s(pseudotime, tissue, bs = "fs", k = 5) | 2. Predict and plot per-tissue curves | fs vs s(x, by = tissue) | Multiple smooths and mixing with random effects | Session info
Rendered fromsplines.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-15

sccomp: Differential Composition and Variability Analysis for Single-Cell Data
Why sccomp? | Comprehensive Method Comparison | Scientific Citation | Talk | Installation Guide | Server special requirements: Restricted or read-only environments | Core Functions | Analysis Tutorial | Binary Factor Analysis | From Seurat, SingleCellExperiment, metadata objects | From counts | Outlier Identification | Visualization and Summary Plots | Adding Custom Layers to Boxplots | Model Proportions Directly (e.g. from deconvolution) | Continuous Factor Analysis | Random Effect Modeling (Mixed-Effect Modeling) | Random Intercept Model | Random Effect Model (random slopes) | Nested Random Effects | Result Interpretation and Communication | Contrasts Analysis | Categorical Factor Analysis (Bayesian ANOVA) | Differential Variability Analysis | Recommended Settings for Different Data Types | For Single-Cell RNA Sequencing | For CyTOF and Microbiome Data | MCMC Chain Visualization
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-07-21
Started: 2021-09-19