Why sccomp? | Comprehensive Method Comparison | Scientific Citation | Talk | Installation Guide | Core Functions | Analysis Tutorial | Binary Factor Analysis | From Seurat, SingleCellExperiment, metadata objects | From counts | Outlier Identification | Visualization and Summary Plots | 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
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:
sccomp_2.5.0.tar.gz
sccomp_2.5.0.zip(r-4.7)sccomp_2.5.0.zip(r-4.6)sccomp_2.5.0.zip(r-4.5)
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
Datasets:
- counts_obj - Counts_obj
- no_significance_df - No_significance_df
- sce_obj - Sce_obj
- seurat_obj - Seurat_obj
On BioConductor:sccomp-2.5.0(bioc 3.24)sccomp-2.4.0(bioc 3.23)
bayesianregressiondifferentialexpressionsinglecellmetagenomicsflowcytometryspatialbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation
Last updated from:67110133c8. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 267 | ||
| linux-devel-x86_64 | NOTE | 285 | ||
| source / vignettes | OK | 342 | ||
| linux-release-x86_64 | NOTE | 361 | ||
| macos-release-arm64 | NOTE | 261 | ||
| macos-oldrel-arm64 | NOTE | 222 | ||
| windows-devel | NOTE | 287 | ||
| windows-release | NOTE | 262 | ||
| windows-oldrel | NOTE | 263 | ||
| wasm-release | OK | 174 |
Exports:clear_stan_model_cacheplot_1D_intervalsplot_2D_intervalssccomp_boxplotsccomp_calculate_residualssccomp_estimatesccomp_predictsccomp_proportional_fold_changesccomp_remove_outlierssccomp_remove_unwanted_effectssccomp_remove_unwanted_variationsccomp_replicatesccomp_stan_models_cache_dirsccomp_testsccomp_themesimulate_data
Dependencies:abindBiobaseBiocGenericsbitbit64bootcallrclicliprcpp11crayonDelayedArraydplyrfansifarverforcatsfsgenericsGenomicRangesggplot2ggrepelgluegtablehmsinstantiateIRangesisobandlabelinglatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsotelpatchworkpillarpkgconfigprettyunitsprocessxprogresspspurrrR6RColorBrewerRcppreadrrlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentSparseArraystringistringrSummarizedExperimenttibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrXVector
