ArticleNucleic acids research2022
MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.Experimental & molecular medicine · 2026Review
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- SciGeneX: enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery.Nature communications · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.bioRxiv : the preprint server for biology · 2024Article
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
- scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.PLoS biology · 2023Article
- Normalization of the tumor microenvironment by harnessing vascular and immune modulation to achieve enhanced cancer therapy.Experimental & molecular medicine · 2023Review
- scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data.BMC genomics · 2023Article
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5 authors.
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Abstract
The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For example, there can be cell types not well distinguished by clustering because they largely share the global structure, such as the anterior primitive streak and mid primitive streak cells. If one searches differentially expressed genes (DEGs) solely based on clustering, marker genes for distinguishing these types will be missed. Moreover, clustering depends on many parameters and can often be subjective to manual decisions. To overcome these limitations, we propose MarcoPolo, a method that identifies informative DEGs independently of prior clustering. MarcoPolo sorts out genes by evaluating if the distributions are bimodal, if similar expression patterns are observed in other genes, and if the expressing cells are proximal in a low-dimensional space. Using real datasets with FACS-purified cell labels, we demonstrate that MarcoPolo recovers marker genes better than competing methods. Notably, MarcoPolo finds key genes that can distinguish cell types that are not distinguishable by the standard clustering. MarcoPolo is built in a convenient software package that provides analysis results in an HTML file.
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