Package: scTypeEval 1.1.1

Josep Garnica

scTypeEval: Evaluation of cell type classifications in single-cell transcriptomics

scTypeEval provides tools to evaluate and validate cell type classifications in single-cell transcriptomics when ground truth labels are limited or unavailable. Results are organized in an S4 object that integrates processed data, dimensional reductions, dissimilarity assays, and consistency metrics computed across samples. The workflow includes preprocessing and feature selection, principal component analysis, computation of dissimilarity matrices, internal validation metrics (for example, silhouette-based summaries), and visualization utilities to inspect heatmaps and PCA plots. Functions support common single-cell containers and enable comparison of clustering and labeling strategies across datasets.

Authors:Josep Garnica [aut, cre], Massimo Andreatta [aut], Santiago Carmona [aut]

scTypeEval_1.1.1.tar.gz
scTypeEval_1.1.1.zip(r-4.7)scTypeEval_1.1.1.zip(r-4.6)scTypeEval_0.99.24.zip(r-4.5)
scTypeEval_1.1.1.tgz(r-4.6-any)scTypeEval_0.99.24.tgz(r-4.5-any)
scTypeEval_1.1.1.tar.gz(r-4.7-any)scTypeEval_1.1.1.tar.gz(r-4.6-any)
scTypeEval_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scTypeEval/json (API)

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

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

Datasets:
  • black_list - Default Gene Blacklist for scTypeEval

On BioConductor:scTypeEval-1.1.0(bioc 3.24)scTypeEval-1.0.0(bioc 3.23)

singlecelltranscriptomicsgeneexpressioncellbasedassaysdimensionreductionpreprocessingprincipalcomponent

6.73 score 6 stars 15 scripts 18 exports 79 dependencies

Last updated from:9ec56f727a. Checks:5 WARNING, 2 OK, 2 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING334
source / vignettesOK313
linux-release-x86_64WARNING322
macos-release-arm64WARNING233
macos-oldrel-arm64FAIL77
windows-develWARNING210
windows-releaseWARNING237
windows-oldrelFAIL95
wasm-releaseOK220

Exports:add_dim_reductionadd_gene_listadd_processed_datacreate_scTypeEvalget_consistencyget_hierarchyget_nnload_single_cell_objectplot_heatmapplot_mdsplot_pcarun_dissimilarityrun_gene_markersrun_hvgrun_pcarun_processing_dataset_active_identwrapper_scTypeEval

Dependencies:abindassortheadbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblustercliclustercodetoolscpp11data.tableDelayedArraydplyrdqrngedgeRfarverformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2ggrepelgluegtableigraphIRangesirlbaisobandlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatsmetapodpillarpkgconfigpurrrR6RColorBrewerRcppRcppEigenrlangrsvdS4ArraysS4VectorsS7ScaledMatrixscalesscranscuttleSeqinfoSingleCellExperimentSingleRsitmosnowSparseArraystatmodstringistringrSummarizedExperimenttibbletidyrtidyselecttransportutf8vctrsviridisLitewithrXVector

Quick Start Guide for scTypeEval
Overview | Minimal Workflow | From a Count Matrix | From a Seurat Object | From a SingleCellExperiment Object | Common Use Cases | Compare Multiple Dissimilarity Methods | Evaluate Multiple Consistency Metrics | Visualize Results | Using Marker Genes Instead of HVGs | Focus on Specific Gene Sets | Interpreting Results | What Low Scores Mean | Next Steps for Low-Scoring Cell Types | Available Methods and Metrics | Dissimilarity Methods | Consistency Metrics | Tips and Best Practices | Getting Help | Session Info
Rendered fromquick-start.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-01-27

scTypeEval: Evaluating Cell Type Labels Consistency in scRNA-seq
Introduction | Key Features | Quick Start | Generate Example Data | Core Workflow | Step 1: Create scTypeEval Object | Step 2: Process Data | Step 3: Extract Relevant Features | Highly Variable Genes | Cell Type Marker Genes | Custom Gene Lists (Optional) | Step 4: Dimensional Reduction (Optional but Recommended) | Step 5: Compute Dissimilarity Matrices | Pseudobulk-based Distances | Wasserstein Distance | Reciprocal Classification | View Available Dissimilarity Matrices | Step 6: Compute Consistency Metrics | Compute Silhouette Scores | Compute Neighborhood Purity | Compare Multiple Metrics | Visualization | Dissimilarity Heatmap | Pseudobulk PCA | Interpretation Guidelines | Identifying Problematic Annotations | Recommendations | Session Information | References
Rendered fromscTypeEval.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-01-27