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scFOCAL

Single-Cell Framework for Omics-Connectivity and Analysis via L1000

📖 Full Documentation | 📦 R-Universe | 📄 Nature Communications Paper

Overview

scFOCAL (Single-Cell Framework for Omics-Connectivity and Analysis via L1000) is a computational framework designed for integrative analysis of single-cell transcriptomics and pharmacological perturbation signatures. The package enables systematic identification of drug-sensitive and drug-resistant cell populations within heterogeneous tumor microenvironments by leveraging the LINCS L1000 database of transcriptional consensus signatures (TCS).

Publication

If you use scFOCAL in your research, please cite:

Suter RK, Jermakowicz AM, Veeramachaneni R, et al. Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations. Nature Communications 17, 99 (2026). https://doi.org/10.1038/s41467-025-67783-5

@article{suter2026drug,
  title={Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations},
  author={Suter, Robert K and Jermakowicz, Artem M and Veeramachaneni, Ram and others},
  journal={Nature Communications},
  volume={17},
  pages={99},
  year={2026},
  publisher={Nature Publishing Group}
}

Key Features

  • Drug-Cell Connectivity Analysis: Quantifies transcriptional concordance between single cells and L1000-derived drug response signatures using Spearman correlation
  • Disease Signature Generation: Computes cell-type-specific differential expression signatures using MAST statistical framework
  • Resistance Prediction: Stratifies tumor cell populations into drug-sensitive and drug-resistant subsets based on pharmacotranscriptomic profiles
  • Combination Therapy Discovery: Identifies synergistic drug combinations through differential connectivity analysis with limma-based linear modeling
  • Interactive Visualization: Provides a comprehensive Shiny-based graphical interface for exploratory analysis

Installation 

install.packages("scFOCAL", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub 

# Install devtools if not available
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

# Install scFOCAL
devtools::install_github("Zaoqu-Liu/scFOCAL")

Dependencies

scFOCAL requires R ≥ 4.3.0 and depends on several Bioconductor packages. Install them with:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("MAST", "edgeR", "ComplexHeatmap", "EnhancedVolcano"))

Note: Seurat is required but listed as a suggested package to allow flexibility in version selection:

# For Seurat v4 (recommended for maximum compatibility)
remotes::install_version("Seurat", version = "4.4.0")

# Or for Seurat v5
install.packages("Seurat")

Quick Start 

library(scFOCAL)

# Launch the interactive Shiny application
runscFOCAL()

Workflow

Input Requirements

scFOCAL accepts single-cell RNA sequencing data as a preprocessed Seurat object (.rds format) with: - Normalized and scaled expression data - Cell type annotations in metadata - Dimensional reduction embeddings (UMAP/tSNE)

Analysis Pipeline

  1. Data Upload: Load your Seurat object containing scRNA-seq data
  2. Pre-processing: Define control (non-malignant) and test (tumor) cell populations
  3. Disease Signatures: Compute differential expression signatures between tumor and control populations
  4. Drug-Cell Connectivity: Calculate Spearman correlations between single-cell expression profiles and L1000 drug signatures
  5. In Silico Perturbation: Identify sensitive/resistant populations and discover combination therapies
Detailed Tutorial

Step 1: Data Upload

Upload a Seurat object saved as an RDS file:

Step 2: Pre-processing

Select cell populations for analysis:

Step 3: Disease Signature Generation

Compute cell-type-specific disease signatures using MAST:

Step 4: Drug-Cell Connectivity Analysis

Calculate drug sensitivity scores:

Step 5: Results Visualization

Explore pharmacotranscriptomic insights:

Example Data

Download example datasets for testing:

Online Version

scFOCAL is also available as a web application:

🌐 https://robert-k-suter.shinyapps.io/isosceles/

Note: Due to the computational requirements of scRNA-seq analysis, local installation is recommended for large datasets.

Technical Details

Statistical Methods

  • Differential Expression: MAST (Model-based Analysis of Single-cell Transcriptomics) with subject-level random effects
  • Drug-Cell Connectivity: Spearman rank correlation between gene expression vectors and L1000 consensus signatures
  • Differential Connectivity: Fisher’s Z-transformation followed by limma linear modeling with empirical Bayes moderation
  • Reversal Scoring: Ratio of discordant to concordant gene expression changes

Data Sources

The package includes pre-processed L1000 transcriptional consensus signatures from the LINCS program (2017 release), comprising 1,679 compounds with genome-wide perturbation profiles.

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/NewFeature)
  3. Commit changes (git commit -m 'Add NewFeature')
  4. Push to branch (git push origin feature/NewFeature)
  5. Open a Pull Request

License

This software is provided by Georgetown University for non-commercial use only. Commercial use requires a separate license agreement. Contact Georgetown University Office of Technology Commercialization for inquiries.

Contact

    

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