scECODA
This is the development version of scECODA; for the stable release version, see scECODA.
All Bioconductor versions of scECODA
3.24 (devel), 3.23 (release)
Single-Cell Exploratory Compositional Data Analysis
Bioconductor version: 3.24 · Package version: 1.1.6
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.
Author: Christian Halter [aut, cre] 
, Massimo Andreatta [aut] , Santiago Carmona [aut] , Swiss Cancer Research Foundation [fnd]
Maintainer: Christian Halter <scecoda.dev at gmail.com>
Citation
From within R, enter citation("scECODA"):
Christian Halter, Massimo Andreatta, Santiago Carmona. scECODA: Single-Cell Exploratory Compositional Data Analysis. doi:10.18129/B9.bioc.scECODA, R package version 1.1.6, https://bioconductor.org/packages/scECODA.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("scECODA")For older versions of R, please refer to the appropriate Bioconductor release.
Details
| Version | 1.1.6 |
| License | GPL-3 + file LICENSE |
| URL | https://github.com/carmonalab/scECODA |
| Bug Reports | https://github.com/carmonalab/scECODA/issues |
| Last updated | 2026-06-19 |
| In Bioconductor since | BioC 3.23 (R-4.6) (less than a year) |
| Downloads rank | 2116 of 2,456 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, Normalization, Preprocessing, PrincipalComponent, SingleCell, Software, Transcriptomics, Visualization |
| Package Short Url | https://bioconductor.org/packages/scECODA/ |
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("scECODA")| scECODA tutorial | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Download
Follow the installation instructions to use this package in your R session.
| Source package | scECODA_1.1.6.tar.gz |
| Windows binary (x86_64) | scECODA_1.1.6.zip |
| macOS binary (arm64) | scECODA_1.1.6.tgz |
| macOS binary (x86_64) | scECODA_1.1.6.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/scECODA |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/scECODA |
| Package Downloads Report | Download Stats |
Dependencies
Depends: R (>= 4.5.0)
Imports: BiocGenerics, cluster, corrplot, DESeq2, dplyr, factoextra (>= 2.0.0), ggplot2, ggpubr, ggrepel, gtools, Matrix, mclust, methods, pheatmap, plotly, rlang, rstatix, S4Vectors, stringr, SummarizedExperiment (>= 1.34.0), tidyr, vegan
Suggests: Seurat (>= 5.0.0), igraph, knitr, rmarkdown, BiocStyle, testthat, scRNAseq