scECODA
This is the development version of scECODA; to use it, please install the devel version of Bioconductor.
Single-Cell Exploratory Compositional Data Analysis
Bioconductor version: Development (3.23)
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("scECODA")):
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("scECODA")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("scECODA")| scECODA.html | HTML | R Script |
| Reference Manual |
Details
| biocViews | CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, Normalization, Preprocessing, PrincipalComponent, SingleCell, Software, Transcriptomics, Visualization |
| Version | 0.99.9 |
| In Bioconductor since | BioC 3.23 (R-4.6) |
| License | GPL-3 + file LICENSE |
| Depends | R (>= 4.6.0) |
| 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 |
| System Requirements | |
| URL | https://github.com/carmonalab/scECODA |
| Bug Reports | https://github.com/carmonalab/scECODA/issues |
See More
| Suggests | Seurat (>= 5.0.0), igraph, knitr, rmarkdown, BiocStyle, testthat, scRNAseq |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | scECODA_0.99.9.tar.gz |
| Windows Binary (x86_64) | |
| macOS Binary (big-sur-x86_64) | |
| macOS Binary (big-sur-arm64) | |
| macOS Binary (sonoma-arm64) | scECODA_0.99.9.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/scECODA |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/scECODA |
| Bioc Package Browser | https://code.bioconductor.org/browse/scECODA/ |
| Package Short Url | https://bioconductor.org/packages/scECODA/ |
| Package Downloads Report | Download Stats |