Package: scFOCAL 0.1.0

scFOCAL: Integration of Drug and Single-Cell Gene Expression Signatures

scFOCAL integrates small molecules transcriptional consensus signatures with single-cell gene expression data to identify cell states targeted or resistant to different perturbations. This information is leveraged by the scFOCAL combination index, which identifies synergistic combinations that maximize the discordance across a heterogenous tumor.

Authors:Zaoqu Liu [aut, cre], Robert K. Suter [aut], Nagi G. Ayad [aut]

scFOCAL_0.1.0.tar.gz


scFOCAL_0.1.0.tar.gz(r-4.6-any)
scFOCAL_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scFOCAL/json (API)

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

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

Datasets:

On CRAN:

Conda:

3.40 score 6 scripts 1 exports 174 dependencies

Last updated from:fb312da417 (on main). Checks:4 FAIL, 2 OK, 3 FAILURE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64FAIL3800
source / vignettesOK339
linux-release-x86_64FAIL3823
macos-release-arm64FAIL3769
macos-oldrel-arm64FAIL3785
windows-develFAILURE3719
windows-releaseFAILURE3727
windows-oldrelFAILURE3724
wasm-releaseOK221

Exports:runscFOCAL

Dependencies:abindaskpassbackportsbase64encBiobaseBiocGenericsbootbroombslibcachemcarcarDatacheckmatecirclizecliclueclustercodetoolscolorspacecommonmarkComplexHeatmapcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDelayedArrayDerivdigestdoBydoParalleldotCall64dplyrDTedgeREnhancedVolcanoevaluatefarverfastmapfieldsfontawesomeforeachforecastforeignFormulafracdifffsgenericsGenomicRangesGetoptLongggalluvialggforceggplot2ggpubrggrepelggsciggsignifGlobalOptionsgluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelimmalme4lmtestlocfitmagrittrmapsMASSMASTMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmimeminqamodelrnlmenloptrnnetnumDerivopensslotelpbkrtestpheatmappillarpkgconfigplotlyplyrpngpolyclippolynomprettyunitsprogresspromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rjsonrlangrmarkdownrpartrstatixrstudioapiS4ArraysS4VectorsS7sassscalesSeqinfoshapeshinyshinydashboardshinythemesSingleCellExperimentsourcetoolsspamSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsyssystemfontstibbletidyrtidyselecttimeDatetinytextweenrurcautf8vctrsviridisviridisLitewithrxfunxtableXVectoryamlzoo

Case Study: Glioblastoma Analysis
Background | Study Overview | Dataset Description | Research Questions | Analysis Workflow | Step 1: Data Preparation | Step 2: Define Cell Populations | Step 3: Drug-Cell Connectivity Analysis | Step 4: Sensitive vs Resistant Classification | Step 5: Differential Connectivity Analysis | Key Findings | 1. Transcriptional State-Specific Drug Sensitivity | 2. Combination Therapy Candidates | 3. Patient-Level Heterogeneity | Biological Validation | Correlation with Known Resistance Markers | Conclusions | References | Session Info
Rendered fromcase-study-gbm.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2026-02-03

Algorithm Principles
Overview | 1. Drug-Cell Connectivity Score | Concept | Mathematical Formulation | Interpretation | 2. Fisher's Z-Transformation | Purpose | Properties | Variance Property | 3. Differential Connectivity Analysis | Linear Model Framework | Empirical Bayes Moderation | 4. Disease Signature Reversal Score | Algorithm | 5. MAST Differential Expression | Model Structure | Advantages for Single-Cell Data | 6. Computational Complexity | Time Complexity Analysis | Memory Requirements | Summary | References | Session Info
Rendered fromalgorithm-principles.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2026-02-03

Quick Start Guide
Introduction | Installation | From R-Universe (Recommended) | From GitHub | Dependencies | Launching scFOCAL | Workflow Overview | Step 1: Data Upload | Step 2: Pre-processing | Step 3: Disease Signature Generation | Step 4: Drug-Cell Connectivity Analysis | Step 5: Results Analysis | Built-in Data | Example Dataset | Next Steps | Citation | Session Info
Rendered fromquick-start.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2026-02-03

Statistical Framework
Introduction | 1. Spearman Rank Correlation | Theoretical Foundation | Formula | Demonstration | 2. Fisher's Z-Transformation | Why Transform? | Statistical Properties | Standard Error | 3. limma Linear Models | Design Matrix Construction | Model Fitting | Empirical Bayes | 4. Multiple Testing Correction | The Problem | FDR Control | 5. Effect Size Interpretation | Log Fold Change in Z-space | Cohen's d Equivalent | 6. Power Analysis | Sample Size Considerations | Recommended Sample Sizes | 7. Quality Control Metrics | Gene Coverage | Connectivity Distribution QC | Summary Statistics Table | Best Practices | Session Info
Rendered fromstatistical-framework.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2026-02-03

Visualization Gallery
Introduction | 1. Dimensional Reduction Plots | UMAP with Cell Type Annotations | UMAP with Drug Connectivity | 2. Violin Plots | Drug Connectivity by Cell Type | 3. Heatmaps | Disease Signature Heatmap | 4. Volcano Plots | Differential Connectivity Volcano | 5. Reversal Score Visualization | Bar Plot of Top Reversal Compounds | 6. Multi-Panel Publication Figure | Customization Tips | Color Palettes | Export Settings | Session Info
Rendered fromvisualization-gallery.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2026-02-03

Readme and manuals

Help Manual

Help pageTopics
L1000_compoundsL1000_compounds
L1000_genesL1000_genes
LINCS.ResponseSigsLINCS.ResponseSigs
Launch scFOCALrunscFOCAL