ArticleGenome research2021
A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- A 3D genome atlas of human tonsil and the role of loop extrusion in B cell somatic hypermutation.Science (New York, N.Y.) · 2026Article
- Optimal gene panel selection for targeted spatial transcriptomics experiments.Nucleic acids research · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- Article
- Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology.bioRxiv : the preprint server for biology · 2026Article
- Single-nucleus transcriptome atlas of orbitofrontal cortex in ALS with a deep learning-based decoding of alternative polyadenylation mechanisms.Cell genomics · 2025Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- Optimal Gene Panel Selection for Targeted Spatial Transcriptomics Experiments.bioRxiv : the preprint server for biology · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- Non-disruptive 3D profiling of combinations of epigenetic marks in single cells.bioRxiv : the preprint server for biology · 2025Article
- A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography.bioRxiv : the preprint server for biology · 2025Article
- Deconvolution of cell types and states in spatial multiomics utilizing TACIT.Nature communications · 2025Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.Nature methods · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.Methods in molecular biology (Clifton, N.J.) · 2025Article
- High-precision lung cancer subtype diagnosis on imbalanced exosomal data via Exo-LCClassifier.Frontiers in genetics · 2025Article
- Probe set selection for targeted spatial transcriptomics.Nature methods · 2024Article
- A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation.Nature genetics · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.
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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.