Network based simultaneous embedding of cells and marker genes from scRNA-seq studies

The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layout algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (t-SNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-08-31
DOI
https://doi.org/10.1093/bib/bbaf537
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
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article

Network based simultaneous embedding of cells and marker genes from scRNA-seq studies

Abhishek Halder, Krishan Gupta, Tanmoy Chakraborty, Gaurav Ahuja et al.
Briefings in Bioinformatics
Single-cell and spatial transcriptomics
article

Network based simultaneous embedding of cells and marker genes from scRNA-seq studies

Abhishek Halder, Krishan Gupta, Tanmoy Chakraborty, Gaurav Ahuja, Aayushi Mittal, Bernadette Mathew, Sakshi Gujral, N Bhattacharya, Colleen C. Nelson, Debajyoti Sinha, Stuti Kumari, Swagatam Chakraborti
article en

Abstract

The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layout algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (t-SNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.

Briefings in BioinformaticsVol. 26(5)
Indraprastha Institute of Information Technology Delhi (IN), Boston Children's Hospital (US), Inserm (FR), Queensland University of Technology (AU), Princess Alexandra Hospital (AU), Institut de Transplantation Urologie en Nephrologie (FR), Centre Nantais de Sociologie (FR), Indian Institute of Technology Delhi (IN), Nantes Université (FR)
Openalex Percentile: Top 75%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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Network based simultaneous embedding of cells and marker genes from scRNA-seq studies — Abhishek Halder, Krishan Gupta, et al. · Briefings in Bioinformatics (2025) | TGRS Research Map | TGRS