scECODA
Single-Cell Exploratory Compositional Data Analysis
Bioconductor version: 3.24 · Package version: 1.1.6
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scECODA")Details
| Maintainer | Christian Halter <scecoda.dev@gmail.com> |
| Author | Christian Halter [aut, cre] (ORCID: <https://orcid.org/0009-0009-5479-2246>), Massimo Andreatta [aut] (ORCID: <https://orcid.org/0000-0002-8036-2647>), Santiago Carmona [aut] (ORCID: <https://orcid.org/0000-0002-2495-0671>), Swiss Cancer Research Foundation [fnd] |
| License | GPL-3 + file LICENSE |
| URL | https://github.com/carmonalab/scECODA |
| Bug Reports | https://github.com/carmonalab/scECODA/issues |
| 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/ |
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.
Documentation
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 |
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