ArticleMolecular systems biology2019
Combinatorial prediction of marker panels from single-cell transcriptomic data.
Article in Molecular systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
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- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- A Mouthful of Genomic Data: Single-Cell Insights into Salivary Gland Biology and Disease.Biology · 2026Review
- snRNA-seq of long-preserved FFPE samples from colorectal liver metastasis lesions with diverse prognoses.Scientific data · 2024Article
- Hierarchical marker genes selection in scRNA-seq analysis.PLoS computational biology · 2024Article
- Probe set selection for targeted spatial transcriptomics.Nature methods · 2024Article
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.NPJ systems biology and applications · 2024Article
- scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.Briefings in bioinformatics · 2024Article
- An intestinal TNature immunology · 2024Article
- CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes.Scientific reports · 2024Article
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.bioRxiv : the preprint server for biology · 2024Article
- Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells.Scientific data · 2024Article
- Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model.BMC genomics · 2024Article
- Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes.Nature communications · 2024Article
- CaSSiDI: novel single-cell "Cluster Similarity Scoring and Distinction Index" reveals critical functions for PirB and context-dependent Cebpb repression.Cell death and differentiation · 2024Article
- Distinct ontogenetic lineages dictate cDC2 heterogeneity.Nature immunology · 2024Article
- A comparison of marker gene selection methods for single-cell RNA sequencing data.Genome biology · 2024Article
1 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a stand-alone software package (https://github.com/MSingerLab/COMETSC).
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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.