Package: sccomp 1.11.0
sccomp: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data
A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Authors:
sccomp_1.11.0.tar.gz
sccomp_1.11.0.zip(r-4.5)sccomp_1.11.0.zip(r-4.4)sccomp_1.11.0.zip(r-4.3)
sccomp_1.11.0.tgz(r-4.4-any)sccomp_1.11.0.tgz(r-4.3-any)
sccomp_1.11.0.tar.gz(r-4.5-noble)sccomp_1.11.0.tar.gz(r-4.4-noble)
sccomp_1.11.0.tgz(r-4.4-emscripten)sccomp_1.11.0.tgz(r-4.3-emscripten)
sccomp.pdf |sccomp.html✨
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
- counts_obj - Counts_obj
- multipanel_theme - Multipanel_theme
- sce_obj - Sce_obj
- seurat_obj - Seurat_obj
On BioConductor:sccomp-1.11.0(bioc 3.21)sccomp-1.10.0(bioc 3.20)
bayesianregressiondifferentialexpressionsinglecellbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation
Last updated 2 months agofrom:4f2a9618bd. Checks:OK: 1 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 30 2024 |
R-4.5-win | WARNING | Nov 30 2024 |
R-4.5-linux | WARNING | Nov 30 2024 |
R-4.4-win | WARNING | Nov 30 2024 |
R-4.4-mac | WARNING | Nov 30 2024 |
R-4.3-win | WARNING | Nov 30 2024 |
R-4.3-mac | WARNING | Nov 30 2024 |
Exports:get_output_samplesplot_1D_intervalsplot_2D_intervalssccomp_boxplotsccomp_calculate_residualssccomp_estimatesccomp_predictsccomp_proportional_fold_changesccomp_remove_outlierssccomp_remove_unwanted_variationsccomp_replicatesccomp_testsimulate_data
Dependencies:abindaskpassBiobaseBiocGenericsbitbit64bootcallrclicliprcodetoolscolorspacecpp11crayoncurlDelayedArraydigestdotCall64dplyrfansifarverforcatsfsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelglobalsgluegtablehmshttrinstantiateIRangesisobandjsonlitelabelinglatticelifecyclelistenvmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslparallellypatchworkpillarpkgconfigprettyunitsprocessxprogressprogressrpspurrrR6RColorBrewerRcppRcppEigenreadrrlangS4ArraysS4VectorsscalesSeuratObjectSingleCellExperimentspspamSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselecttzdbUCSC.utilsutf8vctrsviridisLitevroomwithrXVectorzlibbioc
Readme and manuals
Help Manual
Help page | Topics |
---|---|
counts_obj | counts_obj |
Get Output Samples from a Stan Fit Object | get_output_samples |
multipanel_theme | multipanel_theme |
Plot 1D Intervals for Cell-group Effects | plot_1D_intervals |
Plot 2D Intervals for Mean-Variance Association | plot_2D_intervals |
Plot Boxplot of Cell-group Proportion | plot_boxplot |
Plot Scatterplot of Cell-group Proportion | plot_scatterplot |
plot | plot.sccomp_tbl |
sccomp_boxplot | sccomp_boxplot |
Calculate Residuals Between Observed and Predicted Proportions | sccomp_calculate_residuals |
Main Function for SCCOMP Estimate | sccomp_estimate |
sccomp_predict | sccomp_predict |
Calculate Proportional Fold Change for sccomp Data | sccomp_proportional_fold_change |
sccomp_remove_outliers main | sccomp_remove_outliers |
sccomp_remove_unwanted_variation | sccomp_remove_unwanted_variation |
sccomp_replicate | sccomp_replicate |
sccomp_test | sccomp_test |
sce_obj | sce_obj |
seurat_obj | seurat_obj |
simulate_data | simulate_data |