The workflow begins with a bimodal index-driven procedure for selecting informative features. It then uses feature-weighted kernel density estimation and displays the resulting density as interactive contours that can be gated. The application was built in R Shiny and supports both gene-expression measurements and combined transcript-protein data, allowing the same interactive framework to be used for scRNA-seq and CITE-seq datasets.

The reported aim is to combine the interpretability of expert-guided population selection with a systematic basis for defining and reproducing those selections. In a software application study using CITE-seq peripheral blood mononuclear cell data from participants in the Cardiovascular Assessment Virginia cohort, scDIG resolved transcriptionally distinct CD4-positive T-cell subpopulations within continuous embeddings that conventional clustering did not readily capture. Applied to RNA-expression data, the tool also identified GZMB-enriched cytotoxic lymphocyte subsets.

The authors also conducted sensitivity analyses to assess whether the gates identified in two-dimensional views corresponded to coherent populations in the original high-dimensional feature space. They reported that the defined gates corresponded to coherent cellular groupings, supporting the use of interactive density contours as a way to connect visual population definition with underlying measurements. scDIG is freely available for non-commercial use, and its source code is available through GitLab.

Its central contribution is an interactive method for defining cell populations in single-cell embeddings while retaining researcher oversight. By translating weighted density estimates into adjustable contours, the application provides a practical framework for investigating distinct cell groups in scRNA-seq and CITE-seq data and for evaluating whether selected gates remain coherent in high-dimensional feature space.

Subject of Research: Interactive density-based gating of single-cell proteomic and transcriptomic data

Article Title: scDIG: an R Shiny application for interactive density-based gating of single-cell proteomic and transcriptomic data

Article References: Bombina, P., Bellapu, A., Fogel, L., Coombes, K. R., & Ley, K. (2026). scDIG: an R Shiny application for interactive density-based gating of single-cell proteomic and transcriptomic data. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06718-3

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06718-3

Keywords: scDIG, single-cell analysis, scRNA-seq, CITE-seq, cell gating, kernel density estimation, R Shiny, bioinformatics, cytotoxic lymphocytes, software, Shiny, application