This package is considered a duplicate. The official version of this package is found at:https://bioc.r-universe.dev/scECODA

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_0.99.5.zip(r-4.5)
scECODA_1.1.6.tgz(r-4.6-any)scECODA_0.99.5.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✨
DESCRIPTION |NEWS
card.svg |card.png
scECODA/json (API)

# Install 'scECODA' in R:

install.packages('scECODA', repos = c('https://carmonalab.r-universe.dev', 'https://cloud.r-project.org'))

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

Datasets:

example_data - Example Data for scECODA

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

software singlecell transcriptomics cellbasedassays normalization preprocessing visualization clustering dimensionreduction featureextraction principalcomponent

6.17 score 9 stars 6 scripts 22 exports 154 dependencies

Last updated from:64cdfaa183. Checks:5 WARNING, 2 OK, 2 FAIL. Indexed: no.

Target	Result	Time
linux-devel-x86_64	WARNING	404
source / vignettes	OK	437
linux-release-x86_64	WARNING	380
macos-release-arm64	WARNING	256
macos-oldrel-arm64	FAIL	96
windows-devel	WARNING	1311
windows-release	WARNING	1444
windows-oldrel	FAIL	1093
wasm-release	OK	241

Exports:calc_anosim calc_ari calc_clr calc_freq calc_modularity calc_sil calculate_pseudobulk deseq2_normalize ecoda find_hvcs get_celltype_counts get_celltype_variances get_hvcs get_sample_metadata plot_barplot plot_boxplot plot_corr plot_heatmap plot_pca plot_pca3d plot_varmean replace_zeros

Dependencies:abind askpass backports base64enc BH Biobase BiocGenerics BiocParallel boot broom bslib cachem car carData cli cluster codetools colorspace corrplot cowplot cpp11 crosstalk curl data.table DelayedArray dendextend Deriv DESeq2 digest doBy dplyr DT ellipse emmeans estimability evaluate factoextra FactoMineR farver fastmap flashClust fontawesome forecast formatR Formula fracdiff fs futile.logger futile.options generics GenomicRanges ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable gtools highr htmltools htmlwidgets httr IRanges irlba isoband jquerylib jsonlite knitr labeling lambda.r later lattice lazyeval leaps lifecycle lme4 lmtest locfit magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats mclust memoise mgcv microbenchmark mime minqa modelr multcompView mvtnorm nlme nloptr nnet numDeriv openssl otel pbkrtest permute pheatmap pillar pkgconfig plotly polynom promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rmarkdown rstatix S4Arrays S4Vectors S7 sass scales scatterplot3d Seqinfo snow SparseArray SparseM stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect timeDate tinytex urca utf8 vctrs vegan viridis viridisLite withr xfun XVector yaml zoo

scECODA tutorial

Rendered fromscECODA.rmdusingknitr::rmarkdown

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

Help page	Topics
Analysis of Similarities (ANOSIM) R score	calc_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 Score	calc_modularity
Calculate Average Silhouette Width	calc_sil
Calculate Pseudobulk from Count Matrix	calculate_pseudobulk
Compute K-Nearest Neighbors (KNN) from Distance Matrix	compute_KNN_from_dist
Compute Shared Nearest Neighbor (SNN) Graph	compute_snn_graph
Reshapes ECODA data into a long format for plotting and analysis.	create_long_data
DESeq2 Normalization of Pseudobulk Data	deseq2_normalize
Create an SummarizedExperiment object from various data types	ecoda
Core constructor for SummarizedExperiment objects from count/frequency matrices	ecoda_helper
Example Data for scECODA	example_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 objects	get_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 Matrix	plot_corr
Generates a Heatmap of Cell Abundance Data from an SummarizedExperiment assay.	plot_heatmap
Plot Principal Component Analysis and Calculate Clustering Scores	plot_pca
Plot 3-dimensional interactive Principal Component Analysis plot	plot_pca3d
Generates a Mean-Variance Plot for CLR-transformed Cell Type Data.	plot_varmean
Replace zero values in count or frequency data	replace_zeros

Package: scECODA 1.1.6

scECODA: Single-Cell Exploratory Compositional Data Analysis

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