ArticleNPJ systems biology and applications2024
MarkerMap: nonlinear marker selection for single-cell studies.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 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
- scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.Briefings in bioinformatics · 2026Article
- CORTADO: hill climbing optimization for cell-type specific marker gene discovery and clustering accuracy improvement.Bioinformatics advances · 2026Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals.Scientific reports · 2024Article
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.NPJ systems biology and applications · 2024Article
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.bioRxiv : the preprint server for biology · 2024Article
- Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.BMC methods · 2024Article
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Authors and funding
5 authors.
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Abstract
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.
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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.