ArticlePLoS computational biology2019
SCMarker: Ab initio marker selection for single cell transcriptome profiling.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- 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
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data.Nature communications · 2026Article
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- Redefining the high variable genes by optimized LOESS regression with positive ratio.BMC bioinformatics · 2025Article
- Hierarchical marker genes selection in scRNA-seq analysis.PLoS computational biology · 2024Article
- CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes.Scientific reports · 2024Article
- A comparison of marker gene selection methods for single-cell RNA sequencing data.Genome biology · 2024Article
- cellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes.Research square · 2024Article
- The spatiotemporal dynamics of spatially variable genes in developing mouse brain revealed by a novel computational scheme.Cell death discovery · 2023Article
- On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.PloS one · 2023Article
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
- A Comparison of the Various Methods for Selecting Features for Single-Cell RNA Sequencing Data in Alzheimer's Disease.Advances in experimental medicine and biology · 2023Article
- FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.Interdisciplinary sciences, computational life sciences · 2022Article
- Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods.The New phytologist · 2022Article
- Future of biomedical, agricultural, and biological systems research using domesticated animals.Biology of reproduction · 2022Article
- Statistics or biology: the zero-inflation controversy about scRNA-seq data.Genome biology · 2022Review
- geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.Genome biology · 2021Article
- Feature selection revisited in the single-cell era.Genome biology · 2021Review
- Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.