Package: SlimR 1.2.0

SlimR: Adaptive Machine Learning Powered, Context Matching Tool for Single-Cell and Spatial Transcriptomics Annotation

Annotates single-cell and spatial-transcriptomic (ST) data using context-matching marker datasets. It creates a unified marker list (`Markers_list`) from multiple sources: built-in curated databases ('Cellmarker2', 'PanglaoDB', 'ScType', 'CellTypist', 'scBaseCount', 'scIBD', 'TCellSI', 'PCTIT', 'PCTAM'), Seurat objects with cell labels, or user-provided Excel tables. SlimR first uses adaptive machine learning for parameter optimization, and then offers two automated annotation approaches: 'cluster-based' and 'per-cell'. Cluster-based annotation assigns one label per cluster, expression-based probability calculation, and AUC validation. Per-cell annotation assigns labels to individual cells using three scoring methods with adaptive thresholds and ratio-based confidence filtering, plus optional UMAP spatial smoothing, making it ideal for heterogeneous clusters and rare cell types. The package also supports semi-automated workflows with heatmaps, feature plots, and combined visualizations for manual annotation. For more information, see the package documentation at <https://github.com/zhaoqing-wang/SlimR>.

Authors:Zhaoqing Wang [aut, cre]

SlimR_1.2.0.tar.gz
SlimR_1.2.0.zip(r-4.7-any)SlimR_1.2.0.zip(r-4.6-any)SlimR_1.2.0.zip(r-4.5-any)
SlimR_1.2.0.tgz(r-4.6-any)SlimR_1.2.0.tgz(r-4.5-any)
SlimR_1.2.0.tar.gz(r-4.7-any)SlimR_1.2.0.tar.gz(r-4.6-any)
SlimR_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SlimR/json (API)

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

Bug tracker:https://github.com/zhaoqing-wang/slimr/issues

Datasets:

On CRAN:

Conda:

5.73 score 8 stars 54 scripts 401 downloads 26 exports 151 dependencies

Last updated from:59dfb7f36d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK268
source / vignettesOK278
linux-releaseOK265
macos-releaseOK206
macos-oldrelOK155
windows-develOK235
windows-releaseOK200
windows-oldrelOK211
wasm-releaseOK170

Exports:Celltype_AnnotationCelltype_annotation_Cellmarker2Celltype_Annotation_CombinedCelltype_annotation_ExcelCelltype_Annotation_FeaturesCelltype_Annotation_HeatmapCelltype_annotation_PanglaoDBCelltype_Annotation_PerCellCelltype_annotation_SeuratCelltype_CalculateCelltype_Calculate_PerCellCelltype_CompareCelltype_VerificationCelltype_Verification_PerCellCompute_Gene_AUC_ROCCompute_Gene_PRCMarkers_filter_Cellmarker2Markers_filter_PanglaoDBMarkers_filter_ScTypepaletteDiscreteParameter_CalculatePlot_Group_Gene_HeatmapPlot_Hierarchy_ProportionPlot_Voronoi_diagramRead_excel_markersRead_seurat_markers

Dependencies:abindaskpassbase64encBHbitopsbslibcachemcaToolscellrangercliclustercodetoolscommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhmshtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypheatmappillarpkgconfigplotlyplyrpngpolyclipprettyunitsprogressprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppThreadRcppTOMLreadxlrematchreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS7sassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
ArchR colour palettesArchRPalettes
Calculate Cluster Variability (Use in package)calculate_cluster_variability
Counts average expression of gene set (Use in package)calculate_expression
Calculate Expression Distribution Skewness (Use in package)calculate_expression_skewness
Calculate gene set expression and infer probabilities with control datasets (Use in package)calculate_probability
Cellmarker2 datasetCellmarker2
Cellmarker2 raw datasetCellmarker2_raw
Cellmarker2 tableCellmarker2_table
Annotate Seurat Object with SlimR Cell Type PredictionsCelltype_Annotation
Uses "marker_list" from Cellmarker2 for cell annotationCelltype_annotation_Cellmarker2
Uses "marker_list" to generate combined plot for cell annotationCelltype_Annotation_Combined
Uses "marker_list" from Excel input for cell annotationCelltype_annotation_Excel
Annotate cell types using features plot with different marker databasesCelltype_Annotation_Features
Uses "marker_list" to generate heatmap for cell annotationCelltype_Annotation_Heatmap
Uses "marker_list" from PanglaoDB for cell annotationCelltype_annotation_PanglaoDB
Annotate Seurat Object with Per-Cell SlimR PredictionsCelltype_Annotation_PerCell
Uses "marker_list" from Seurat object for cell annotationCelltype_annotation_Seurat
Uses "marker_list" to calculate probability, prediction results, AUC and generate heatmap for cell annotationCelltype_Calculate
Per-cell annotation using marker expression and optional UMAP spatial smoothingCelltype_Calculate_PerCell
Compare cell type labels across two single-cell datasets after aligning cell barcodesCelltype_Compare
Perform cell type verification and generate the validation dotplotCelltype_Verification
Verify per-cell annotations with marker expression dotplotCelltype_Verification_PerCell
List of cell type markers from the CellTypist organ atlasCellTypist
Compute Adaptive Parameters Based on Dataset Features (Use in package)compute_adaptive_parameters
Compute AUC and Optionally Plot ROC Curve for a Single GeneCompute_Gene_AUC_ROC
Compute PR-AUC and Optionally Plot Precision-Recall Curve for a Single GeneCompute_Gene_PRC
Estimate Batch Effect Strength (Use in package)estimate_batch_effect
Extract Dataset Characteristics for Adaptive Parameter Calculation (Use in package)extract_dataset_features
Create Marker_list from the Cellmarkers2 databaseMarkers_filter_Cellmarker2
Create Marker_list from the PanglaoDB databaseMarkers_filter_PanglaoDB
Create Marker_list from the ScType databaseMarkers_filter_ScType
List of Macrophage subtype markers in the article "Macrophage diversity in cancer revisited in the era of single-cell omics"Markers_list_PCTAM
List of T cell subtype markers in the article "Pan-cancer single cell landscape of tumor-infiltrating T cells"Markers_list_PCTIT
List of cell type markers from scIBDMarkers_list_scIBD
List of T cell subtype markers in the article TCellSIMarkers_list_TCellSI
PanglaoDB datasetPanglaoDB
PanglaoDB raw datasetPanglaoDB_raw
PanglaoDB tablePanglaoDB_table
Adaptive Parameter Tuning for Single-Cell Data Annotation in SlimRParameter_Calculate
Per-Cell Annotation Workflow Examplepercell_workflow
Plot Grouped Gene Z-Score Heatmap with Optional Integrated Score RowPlot_Group_Gene_Heatmap
Plot a Hierarchical Cell Type Tree with Optional Group Proportion HeatmapPlot_Hierarchy_Proportion
Generate a Weighted Voronoi Treemap for Hierarchical Cell TypesPlot_Voronoi_diagram
Plot Method for pheatmap Objectsplot.pheatmap
Create "Marker_list" from Excel files ".xlsx"Read_excel_markers
Create "Marker_list" from Seurat objectRead_seurat_markers
scBaseCount datasetscBaseCount
ScType datasetScType
ScType raw datasetScType_raw
ScType metadata tableScType_table