Package: scDiagnostics 1.7.15

Anthony Christidis

scDiagnostics: Cell type annotation diagnostics

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

Authors:Anthony Christidis [aut, cre], Andrew Ghazi [aut], Smriti Chawla [aut], Nitesh Turaga [ctb], Ludwig Geistlinger [aut], Robert Gentleman [aut]

scDiagnostics_1.7.15.tar.gz
scDiagnostics_1.7.15.zip(r-4.7-any)scDiagnostics_1.7.15.zip(r-4.6-any)scDiagnostics_1.7.15.zip(r-4.5-any)
scDiagnostics_1.7.15.tgz(r-4.6-any)scDiagnostics_1.7.15.tgz(r-4.5-any)
scDiagnostics_1.7.15.tar.gz(r-4.7-any)scDiagnostics_1.7.15.tar.gz(r-4.6-any)
scDiagnostics_1.7.15.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scDiagnostics/json (API)

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

Bug tracker:https://github.com/ccb-hms/scdiagnostics/issues

Pkgdown/docs site:https://ccb-hms.github.io

On BioConductor:scDiagnostics-1.7.3(bioc 3.24)scDiagnostics-1.6.1(bioc 3.23)

annotationclassificationclusteringgeneexpressionrnaseqsinglecellsoftwaretranscriptomics

8.29 score 17 stars 76 scripts 306 downloads 33 exports 81 dependencies

Last updated from:bee3ff5880. Checks:1 WARNING, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING309
linux-develOK803
source / vignettesOK406
linux-releaseOK695
macos-releaseOK555
macos-oldrelOK544
windows-develOK1564
windows-releaseOK2504
windows-oldrelOK2557
wasm-releaseOK267

Exports:boxplotPCAcalculateAveragePairwiseCorrelationcalculateCategorizationEntropycalculateCellDistancescalculateCellDistancesSimilaritycalculateCellSimilarityPCAcalculateCramerPValuecalculateDiscriminantSpacecalculateGeneShiftscalculateGraphIntegrationcalculateHotellingPValuecalculateHVGOverlapcalculateMMDPValuecalculateReconstructionErrorcalculateSIRSpacecalculateVarImpOverlapcalculateWassersteinDistancecompareMarkerscomparePCAcomparePCASubspacedetectAnomalyhistQCvsAnnotationplotCellTypeMDSplotCellTypePCAplotGeneExpressionDimredplotGeneSetScoresplotMarkerExpressionplotPairwiseDistancesDensityplotQCvsAnnotationprocessPCAprojectPCAprojectSIRregressPC

Dependencies:abindassortheadbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelblusterbootcliclustercodetoolscpp11cramercrayondata.tableDelayedArraydplyrfarverFNNforcatsformatRfutile.loggerfutile.optionsgenericsGenomicRangesGGallyggplot2ggridgesggstatsgluegtablehmsigraphIRangesisobandisotreejsonlitelabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatspatchworkpillarpkgconfigprettyunitsprogresspurrrR6rangerRColorBrewerRcppRcppEigenRhpcBLASctlrlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentsnowSparseArraystringistringrSummarizedExperimenttibbletidyrtidyselecttransportutf8vctrsviridisLitewithrXVector

Case Study: A Disease-Associated Monocyte State in COVID-19
Purpose | The data | Step 1: project | Step 2: detect | Step 3: characterize | R Session Info
Rendered fromCOVIDCaseStudy.Rmdusingknitr::rmarkdown

Last update: 2026-09-21
Started: 2026-09-21

Case Study: An Inflamed Fibroblast State in Spatial Colitis Data
Purpose | The data | Step 1: project | Step 2: detect | Step 3: characterize | R Session Info
Rendered fromMERFISHCaseStudy.Rmdusingknitr::rmarkdown

Last update: 2026-09-21
Started: 2026-09-21

Introduction to scDiagnostics
Purpose | Overview of the workflow | Installation | Installation from Bioconductor (Release) | Installation from GitHub (Development) | A worked example with known ground truth | Finding the function you need | Visualization | Dataset alignment and statistical comparison | Anomaly detection and cell distances | Marker gene alignment | Utilities | R Session Info
Rendered fromIntroduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-21
Started: 2026-09-21

Benchmarking Anomaly Detection Against Ground Truth
Purpose | A live example: a withheld cell type related to a retained one | Baseline: distinct, related, and rare withheld cell types | Sensitivity to label noise, class imbalance, and batch effects | Hyperparameter sensitivity | Combining both methods | R Session Info
Rendered fromZeiselBenchmarking.Rmdusingknitr::rmarkdown

Last update: 2026-09-21
Started: 2026-09-21

Readme and manuals

Help Manual

Help pageTopics
Plot Principal Components for Different Cell TypesboxplotPCA
Calculate Categorization EntropycalculateCategorizationEntropy
Function to Calculate Bhattacharyya Coefficients and Hellinger DistancescalculateCellDistancesSimilarity
Calculate Cramer Test P-Values for Two-Sample Comparison of Multivariate ECDFscalculateCramerPValue
Project Query Data onto a Unified Discriminant Space of Reference DatacalculateDiscriminantSpace plot.calculateDiscriminantSpaceObject
Calculate Top Loading Gene Expression ShiftscalculateGeneShifts plot.calculateGeneShiftsObject
Calculate Graph Community Integration DiagnosticscalculateGraphIntegration plot.calculateGraphIntegrationObject
Perform Hotelling's T-squared Test on PCA Scores for Single-cell RNA-seq DatacalculateHotellingPValue
Calculate the Overlap Coefficient for Highly Variable GenescalculateHVGOverlap
Calculate Maximum Mean Discrepancy P-Values for Two-Sample ComparisoncalculateMMDPValue
Calculate PCA Reconstruction Errors for Out-of-Distribution Anomaly DetectioncalculateReconstructionError plot.calculateReconstructionErrorObject
Calculate Sliced Inverse Regression (SIR) Space for Different Cell TypescalculateSIRSpace plot.calculateSIRSpaceObject
Compare Gene Importance Across Datasets Using Random ForestcalculateVarImpOverlap
Compare Principal Components Analysis (PCA) ResultscomparePCA plot.comparePCAObject
PCA Anomaly Scores via Isolation Forests with VisualizationdetectAnomaly plot.detectAnomalyObject
Histograms: QC Stats and Annotation Scores VisualizationhistQCvsAnnotation
Plot Regression Results on Principal Componentsplot.regressPCObject regressPC
Plot Reference and Query Cell Types using MDSplotCellTypeMDS
Plot Principal Components for Different Cell TypesplotCellTypePCA
Visualize gene expression on a dimensional reduction plotplotGeneExpressionDimred
Visualization of gene sets or pathway scores on dimensional reduction plotplotGeneSetScores
Plot gene expression distribution from overall and cell type-specific perspectiveplotMarkerExpression
Ridgeline Plot of Pairwise Distance AnalysisplotPairwiseDistancesDensity
Scatter plot: QC stats vs Cell Type Annotation ScoresplotQCvsAnnotation
Process PCA for SingleCellExperiment ObjectsprocessPCA
Project Query Data Onto PCA Space of Reference DataprojectPCA
Project Query Data Onto SIR Space of Reference DataprojectSIR