ArticleNature communications2021
DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- TopClust: Topological-based analysis of scRNA-seq data for data-driven identification of clusters and core cluster cells.Biophysical journal · 2026Article
- Single-cell marker gene clustering: A unified deep learning framework for marker gene-based clustering of single-cell RNA-sequencing data.Quantitative biology (Beijing, China) · 2026Article
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- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- CBX3:IL1RN Reflects Distinct Cellular States That Defines the Clinical Outcome of Oral Squamous Cell Carcinoma.Cancer medicine · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- A multimodal deep learning model for predicting impending rupture in symptomatic abdominal aortic aneurysms using CTA and clinical data.Frontiers in cardiovascular medicine · 2026Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- Intrinsic changes in cell differentiation and identity drive impaired wound healing in aged female murine skin.Biogerontology · 2025Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- getDNB: identifying dynamic network biomarkers of hepatocellular carcinoma from time-varying gene regulations utilizing graph embedding techniques for anomaly detection.Bioinformatics (Oxford, England) · 2025Article
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- Analysis of an engineered organoid model of pancreatic cancer identifies hypoxia as a contributing factor in determining transcriptional subtypes.Scientific reports · 2025Article
- SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data.NAR genomics and bioinformatics · 2025Article
- scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference.Scientific reports · 2025Article
- Cross study transcriptomic investigation of Alzheimer's brain tissue discoveries and limitations.Scientific reports · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
- scSAMAC: saliency-adjusted masking induced attention contrastive learning for single-cell clustering.Briefings in bioinformatics · 2025Article
- scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.Interdisciplinary sciences, computational life sciences · 2025Article
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16 authors.
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Abstract
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.
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