Package: scECODA 1.1.6

Christian Halter

scECODA: Single-Cell Exploratory Compositional Data Analysis

The scECODA R package provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data. It implements specialized methods, such as the Centered Log-Ratio (CLR) transformation, to properly analyze proportional data while avoiding the biases introduced by the compositional constraint. The package encapsulates data management, transformation, and analysis into a single SummarizedExperiment object, offering downstream tools for dimensionality reduction via PCA, calculating critical metrics like the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality, and generating high-quality visualizations like heatmaps and scatter plots.

Authors:Christian Halter [aut, cre], Massimo Andreatta [aut], Santiago Carmona [aut], Swiss Cancer Research Foundation [fnd]

scECODA_1.1.6.tar.gz
scECODA_1.1.6.zip(r-4.7)scECODA_1.1.6.zip(r-4.6)scECODA_1.1.6.zip(r-4.5)
scECODA_1.1.6.tgz(r-4.6-any)scECODA_1.1.6.tgz(r-4.5-any)
scECODA_1.1.6.tar.gz(r-4.7-any)scECODA_1.1.6.tar.gz(r-4.6-any)
scECODA_1.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scECODA/json (API)
NEWS

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

Bug tracker:https://github.com/carmonalab/scecoda/issues

Datasets:

On BioConductor:scECODA-1.1.6(bioc 3.24)scECODA-1.0.0(bioc 3.23)

softwaresinglecelltranscriptomicscellbasedassaysnormalizationpreprocessingvisualizationclusteringdimensionreductionfeatureextractionprincipalcomponent

6.17 score 9 stars 6 scripts 22 exports 154 dependencies

Last updated from:64cdfaa183. Checks:1 NOTE, 7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE289
linux-devel-x86_64WARNING373
source / vignettesOK404
linux-release-x86_64WARNING581
macos-release-arm64WARNING242
macos-oldrel-arm64WARNING176
windows-develWARNING1534
windows-releaseWARNING1569
windows-oldrelWARNING1409
wasm-releaseOK243

Exports:calc_anosimcalc_aricalc_clrcalc_freqcalc_modularitycalc_silcalculate_pseudobulkdeseq2_normalizeecodafind_hvcsget_celltype_countsget_celltype_variancesget_hvcsget_sample_metadataplot_barplotplot_boxplotplot_corrplot_heatmapplot_pcaplot_pca3dplot_varmeanreplace_zeros

Dependencies:abindaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelbootbroombslibcachemcarcarDatacliclustercodetoolscolorspacecorrplotcowplotcpp11crosstalkcurldata.tableDelayedArraydendextendDerivDESeq2digestdoBydplyrDTellipseemmeansestimabilityevaluatefactoextraFactoMineRfarverfastmapflashClustfontawesomeforecastformatRFormulafracdifffsfutile.loggerfutile.optionsgenericsGenomicRangesggplot2ggpubrggrepelggsciggsignifgluegridExtragtablegtoolshighrhtmltoolshtmlwidgetshttrIRangesirlbaisobandjquerylibjsonliteknitrlabelinglambda.rlaterlatticelazyevalleapslifecyclelme4lmtestlocfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmclustmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmvtnormnlmenloptrnnetnumDerivopensslotelpbkrtestpermutepheatmappillarpkgconfigplotlypolynompromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrmarkdownrstatixS4ArraysS4VectorsS7sassscalesscatterplot3dSeqinfosnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimeDatetinytexurcautf8vctrsveganviridisviridisLitewithrxfunXVectoryamlzoo

scECODA tutorial

Christian Halter

Rendered fromscECODA.rmdusingknitr::rmarkdownon Jun 19 2026.

Last update: 2026-06-19
Started: 2026-02-11

Readme and manuals

Help Manual

Help pageTopics
Analysis of Similarities (ANOSIM) R scorecalc_anosim
Calculate Adjusted Rand Index (ARI)calc_ari
Perform the Centered Log-Ratio (CLR) transformation.calc_clr
Calculate relative frequencies (percentages) column-wise.calc_freq
Calculate Adjusted Modularity Scorecalc_modularity
Calculate Average Silhouette Widthcalc_sil
Calculate Pseudobulk from Count Matrixcalculate_pseudobulk
Compute K-Nearest Neighbors (KNN) from Distance Matrixcompute_KNN_from_dist
Compute Shared Nearest Neighbor (SNN) Graphcompute_snn_graph
Reshapes ECODA data into a long format for plotting and analysis.create_long_data
DESeq2 Normalization of Pseudobulk Datadeseq2_normalize
Create an SummarizedExperiment object from various data typesecoda
Core constructor for SummarizedExperiment objects from count/frequency matricesecoda_helper
Example Data for scECODAexample_data
Identifies and stores Highly Variable Cell Types (HVCs) in an SummarizedExperiment object.find_hvcs
Get the cell type counts from a long data frame (e.g. seurat object metadata) where each cell is a row.get_celltype_counts
Calculates the variance of cell types across samples.get_celltype_variances
Helper to get assay for ecoda SummarizedExperiment objectsget_ecoda_assay
Selects Highly Variable Cell Types (HVCs) based on variance or count threshold.get_hvcs
Extracts constant metadata for each sample from a cell-level data frame.get_sample_metadata
Generates a Stacked Bar Plot of Cell Type Relative Abundance.plot_barplot
Generates Boxplots for CLR-transformed Cell Type Abundances with Optional Group Comparison.plot_boxplot
Plot Cell Type Correlation Matrixplot_corr
Generates a Heatmap of Cell Abundance Data from an SummarizedExperiment assay.plot_heatmap
Plot Principal Component Analysis and Calculate Clustering Scoresplot_pca
Plot 3-dimensional interactive Principal Component Analysis plotplot_pca3d
Generates a Mean-Variance Plot for CLR-transformed Cell Type Data.plot_varmean
Replace zero values in count or frequency datareplace_zeros