Package: SETA 1.3.0

Kyle Kimler

SETA: Single Cell Ecological Taxonomic Analysis

Tools for compositional and other sample-level ecological analyses and visualizations tailored for single-cell RNA-seq data. SETA includes functions for taxonomizing celltypes, normalizing data, performing statistical tests, and visualizing results. Several tutorials are included to guide users and introduce them to key concepts. SETA is meant to teach users about statistical concepts underlying ecological analysis methods so they can apply them to their own single-cell data.

Authors:Kyle Kimler [aut, cre], Marc Elosua-Bayes [aut]

SETA_1.3.0.tar.gz
SETA_1.3.0.zip(r-4.7)SETA_1.3.0.zip(r-4.6)SETA_1.3.0.zip(r-4.5)
SETA_1.3.0.tgz(r-4.6-any)SETA_1.3.0.tgz(r-4.5-any)
SETA_1.3.0.tar.gz(r-4.7-any)SETA_1.3.0.tar.gz(r-4.6-any)
SETA_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SETA/json (API)

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

On BioConductor:SETA-1.3.0(bioc 3.24)SETA-1.2.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

singlecelltranscriptomicsrnaseqgeneexpressionstatisticalmethoddimensionreductionvisualizationnormalizationdatarepresentationsystemsbiology

5.22 score 4 scripts 19 exports 40 dependencies

Last updated from:cf0dd21901. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE253
linux-devel-x86_64OK375
source / vignettesOK361
linux-release-x86_64OK413
macos-release-arm64OK173
macos-oldrel-arm64OK241
windows-develOK838
windows-releaseOK1171
windows-oldrelOK1126
wasm-releaseOK244

Exports:makeTypeHierarchymockCountmockLongmockSCEmockSeuratresolveGroupsetaALRsetaBalancesetaCLRsetaCountssetaDistancessetaILRsetaLatentsetaLogCPMsetaMetadatasetaPercentsetaTaxonomyDFsetaTransformtaxonomy_to_tbl_graph

Dependencies:abindBiobaseBiocGenericsclicpp11DelayedArraydplyrgenericsGenomicRangesglueigraphIRangeslatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatspillarpkgconfigpurrrR6rlangS4ArraysS4VectorsSeqinfoSingleCellExperimentSparseArraystringistringrSummarizedExperimenttibbletidygraphtidyrtidyselectutf8vctrswithrXVector

Comparing samples with SETA
Introduction | Installation | Load libraries | Load and prepare data | Extracting the Taxonomic Counts Matrix | Prepare metadata | Calculate distances between samples | Perform wilcoxon rank-sum tests on CoDA transformed data | Correlate celltype compositions with metadata | Use SETA transformed data to create predictive models with caret | Conclusion | Session Info
Rendered fromcomparing_samples.Rmdusingknitr::rmarkdown

Last update: 2025-10-22
Started: 2025-04-10

Introduction to SETA ecological transforms and sample-level latent spaces
Introduction | Why use SETA? | Compositional Analysis of Single-Cell RNA-seq Data | What SETA Does Well | Why These Steps Should Be Executed | Package Overview | Installation | Loading the Data | Load and prepare data | Extracting the Taxonomic Counts Matrix | Applying Compositional Transforms | Latent Space Analysis | Visualization | Variance Explained Plot | PCA Scatter plot | Loadings Plot | Conclusion | Session Info
Rendered fromintroductory_vignette.Rmdusingknitr::rmarkdown

Last update: 2025-09-19
Started: 2025-04-08

Multi-Resolution Compositional Analysis in scRNA-seq: Reference Frames with SETA
Introduction | Installation | Load and prepare data | Creating a Taxonomic Data Frame | Visualize the Taxonomy as a Tree via ggraph | Taxonomic Balances with SETA | Transform Counts with a Taxonomic Reference Frame | Visualize Latent Spaces With Different Reference Frames
Rendered fromreference_frames.Rmdusingknitr::rmarkdown

Last update: 2025-09-15
Started: 2025-04-10

Readme and manuals

Help Manual

Help pageTopics
Single Cell Ecological Taxonomic AnalysisSETA-package SETA
Synthetic single-cell, mixture and marker datadata makeTypeHierarchy mockCount mockLong mockSCE mockSeurat
`resolveGroup()` converts a user supplied *group specification* into the column indices of the corresponding leaves in a **counts** taxa matrix. A group specification can be:resolveGroup
Additive Log-Ratio (ALR) TransformsetaALR
User-defined balance transform (geometric-mean log-ratio)setaBalance
Centered Log-Ratio (CLR) Transform Applies a CLR transform to a matrix of counts. Samples should be in rows and taxa (cell types) in columns. For each sample, the transform computes \mathrm{CLR}(x)_i = \log \big( (x_i + c) / g(x + c) \big), where g(x + c) is the geometric mean of the row.setaCLR
Extract Taxonomic Counts from Various Single Cell ObjectssetaCounts
Compute Distance Matrix between SamplessetaDistances
Isometric Log-Ratio (ILR) Transform Applies the ILR transform to an integer counts matrix. For each sample (row), the data are log-transformed (with an optional Box Cox like transformation) then projected onto an orthonormal Helmert basis, reducing dimensionality by one.setaILR
Compute a Latent Space from Transformed CountssetaLatent
log2(CPM) Transform Computes the log2 counts-per-million (CPM) for each sample. Samples are in rows and taxa in columns.setaLogCPM
Extract Sample-Level Metadata from Various ObjectssetaMetadata
Percentage Transform Converts each row (sample) of a counts matrix to percentages of its row sum.setaPercent
Build a taxonomy data frame at multiple resolutionssetaTaxonomyDF
Wrapper for Compositional Transforms with Optional Within-Lineage Resolutions A convenience function that dispatches to one of the transforms: CLR, ALR, ILR, percent, or logCPM. Note that the input 'counts' matrix should have rows as samples and columns as taxa. Optionally, you can supply a taxonomy data frame to perform a within-lineage transform at a specified resolution.setaTransform
Convert Multi-Column Taxonomy to a Single-Root tbl_graph (with node metadata)taxonomy_to_tbl_graph