Package: scPharm 1.0.6

scPharm: Identification of Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers

A computational framework for single-cell RNA-seq data that integrates pharmacogenomics profiles to uncover therapeutic heterogeneity within tumors at single-cell resolution. The tool prioritizes tailored drugs and provides insights into combination therapy regimens and drug toxicity in cancers.

Authors:Zaoqu Liu [aut, cre], Peng Tian [aut, ctb], Jie Zheng [aut, ctb], Haiyun Wang [aut, ctb]

scPharm_1.0.6.tar.gz
scPharm_1.0.6.zip(r-4.7)scPharm_1.0.6.zip(r-4.6)scPharm_1.0.6.zip(r-4.5)
scPharm_1.0.6.tgz(r-4.6-x86_64)scPharm_1.0.6.tgz(r-4.6-arm64)scPharm_1.0.6.tgz(r-4.5-x86_64)scPharm_1.0.6.tgz(r-4.5-arm64)
scPharm_1.0.6.tar.gz(r-4.6-arm64)scPharm_1.0.6.tar.gz(r-4.6-x86_64)
scPharm_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scPharm/json (API)

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

Bug tracker:https://github.com/zaoqu-liu/scpharm/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • bulkdata - Bulk RNA-seq Expression Data for Cancer Cell Lines
  • drug_info - GDSC2 Drug Information
  • gdscdata - GDSC2 Pharmacogenomics Data

On CRAN:

Conda:

openblascpp

3.30 score 4 scripts 5 exports 198 dependencies

Last updated from:33a25f64d3 (on main). Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE663
linux-devel-x86_64NOTE749
source / vignettesOK813
linux-release-arm64NOTE686
linux-release-x86_64NOTE687
macos-release-arm64NOTE418
macos-release-x86_64NOTE861
macos-oldrel-arm64NOTE412
macos-oldrel-x86_64NOTE1017
windows-develNOTE728
windows-releaseNOTE646
windows-oldrelNOTE644
wasm-releaseOK481

Exports:scPharmComboscPharmDrscPharmDsescPharmGenNullDistscPharmIdentify

Dependencies:abindaskpassassortheadbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsbslibcachemCairocaToolsCelliDcliclustercodacodetoolscommonmarkcowplotcpp11crosstalkcurldata.tableDelayedArraydeldirdigestdlmdotCall64dplyrdqrngevaluatefarverfastDummiesfastmapfastmatchfgseafitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomicRangesggbeeswarmggplot2ggrastrggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonlitekernlabKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmcmcMCMCpackmemoisemimeminiUImixtoolsnlmeopensslotelparallelDistparallellypatchworkpbapplypheatmappillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrquantregR6raggRANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppParallelRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsneS4ArraysS4VectorsS7sassScaledMatrixscalesscaterscattermoresctransformscuttlesegmentedSeqinfoSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraySparseMsparseMatrixStatsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsyssystemfontstensortextshapingtibbletictoctidyrtidyselecttinytexumaputf8uwotvctrsviporviridisviridisLitewithrxfunxtableXVectoryamlzoo

Advanced Usage
Introduction | 1. Threshold Calibration with Normal Tissue | Using scPharmGenNullDist | Apply Calibrated Thresholds | Threshold Selection Strategy | 2. Multi-Drug Analysis | Pan-Drug Screening | Drug Class Analysis | Multi-Cancer Analysis | 3. Combination Therapy Optimization | scPharmCombo Analysis | Combination Selection Criteria | 4. Integration with Seurat Workflows | Pre-computed Embeddings | Cluster-Level Analysis | 5. Performance Tuning | Memory Management | Parallel Processing | Parameter Optimization | 6. Custom Drug Signatures | Using External Gene Sets | 7. Quality Control | Pre-analysis Checks | Post-analysis Validation | 8. Troubleshooting | Common Issues | Debug Mode | 9. Exporting Results | To Data Frame | To Seurat Object | Session Info
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Last update: 2026-01-24
Started: 2026-01-24

Algorithm and Methodology
Overview | Workflow Architecture | Step 1: Copy Number Variation Detection | Algorithm Overview | Mathematical Framework | Step 2: Multiple Correspondence Analysis (MCA) | Why MCA? | Mathematical Formulation | C++ Implementation | Step 3: Cell Identity Signatures | Distance Metric | Signature Extraction | Step 4: Gene Set Enrichment Analysis | GSEA Algorithm | Drug Sensitivity Gene Sets | Step 5: Cell Classification | Gaussian Mixture Model | Classification Criteria | Step 6: Drug Scoring Metrics | Drug Prioritization Score (Dr) | Drug Side Effect Score (Dse) | Computational Complexity | References | Session Info
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Last update: 2026-01-24
Started: 2026-01-24

Quick Start Guide
Introduction | Installation | Load Required Packages | Prepare Example Data | Basic Workflow | Step 1: Identify Pharmacological Subpopulations | Step 2: Drug Prioritization | Step 3: Predict Drug Side Effects | Step 4: Identify Drug Combinations | Understanding Output | Cell Labels | Drug Prioritization Score (Dr) | Drug Side Effect Score (Dse) | Parameter Guidelines | Supported Cancer Types | Next Steps | Session Info
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Last update: 2026-01-24
Started: 2026-01-24

Visualization Guide
Introduction | Simulated Analysis Results | 1. Cell Type Distribution | UMAP with Cell Labels | UMAP with Drug Response | UMAP with NES Values | 2. NES Distribution Analysis | Histogram by Cell Type | Density Plot | Violin Plot | 3. Drug Prioritization Visualization | Bar Plot of Dr Scores | Dr vs Dse Scatter Plot | 4. Cell Proportion Analysis | Stacked Bar Plot | Pie Chart | 5. Multi-Drug Comparison | Heatmap of Drug Effects | 6. Combined Dashboard | Summary Panel | 7. Export Functions | Save High-Quality Figures | Session Info
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Last update: 2026-01-24
Started: 2026-01-24