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scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data.
Polina Bombina1, Anusha Bellapu2, Lauren Fogel3
1Department of Biostatistics, Data Science, and Epidemiology, Georgia Cancer Center at Augusta University.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
We developed scDIG, a new tool for analyzing single-cell data. It helps researchers identify distinct cell populations in complex datasets, improving cell classification and the discovery of immune cell states.
Area of Science:
- Single-cell genomics
- Immunology
- Bioinformatics
Background:
- Delineating cell populations in single-cell data is challenging.
- Current methods may not balance expert input with reproducibility.
- Identifying biologically meaningful cell types requires advanced analytical tools.
Purpose of the Study:
- To present scDIG, a Shiny-based tool for cell population delineation.
- To enable reproducible classification of cell types in single-cell data.
- To identify immunologically relevant cell states.
Main Methods:
- scDIG integrates bimodal index-driven feature selection.
- It uses feature-weighted kernel density estimation.
- Interactive contour-based gating is applied to 2D projections of scRNA-seq and CITE-seq data.
Main Results:
- scDIG was applied to CITE-seq PBMC data from the CAVA cohort.
- It resolved transcriptionally distinct CD4+ T cell subpopulations.
- These subpopulations were within continuous embeddings not captured by conventional clustering.
Conclusions:
- scDIG facilitates robust and reproducible classification of single-cell populations.
- The tool aids in identifying immunologically relevant effector states.
- scDIG enhances the analysis of complex single-cell datasets.
