CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity.

Overview

1. Department of Applied Mathematics and Statistics, Johns Hopkins University Baltimore United States
2. Departments of Neurology and Neuroscience, Johns Hopkins University Baltimore United States
3. Department of Natural Sciences, University of Maryland Eastern Shore Princess Anne United States
4. Center for Imaging Science, Johns Hopkins University Baltimore United States
5. Institute for Genome Sciences, University of Maryland School of Medicine Baltimore United States

Abstract

Defining cell classes is central to the analysis of growing single-cell RNA sequencing (scRNA-seq) atlases. Marker genes are most often identified by differential expression (DE) methods that assess genes one at a time, ignoring the redundancy and complementarity revealed when genes are considered jointly. Working with binarized expression data, we instead seek discriminating panels of genes that together are specific to a cell type, framing marker-panel selection as a variant of the minimal set-covering problem in combinatorial optimization. This formulation efficiently searches the vast space of candidate panels, exploits the large cell numbers typical of scRNA-seq, and is robust to zero-inflation. Using blood and brain data, we show that our method, CellCover, reduces gene redundancy and captures cell-class-specific signals distinct from those found by DE. Transfer-learning experiments across mouse, primate, and human data demonstrate that CellCover identifies conserved cell classes in neocortical neurogenesis and tracks developmental progression in progenitors and neurons. Examining outer radial glia markers across mammals, we find that transcriptomic elements of this key cell type likely arose in rodent gliogenic precursors before the full program emerged in the primate lineage.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data availability

While we have not generated any new data in this report, all the public RNA-seq data used in the analyses is freely explorable at NeMO Analytics (https://nemoanalytics.org/expression.html?gene_symbol=SOX2&gene_symbol_exact_match=1&is_multigene=0&layout_id=CellCover). Links to the data we used and original data sources are available at NeMO Analytics. We invite researchers to interrogate all the public data resources we examine here in the NeMO Analytics multi-omics data exploration environment that is designed to provide biologists with no programming expertise the ability to perform powerful analyses across collections of data in brain development (Sonthalia et al., 2026). Users can visualize individual genes (NeMO: Individual genes in cortex (https://nemoanalytics.org/p?&l=CellCover&g=EOMES) and NeMO: Individual genes in blood (https://nemoanalytics.org/p?&l=immune&g=CSF1R)) or groups of genes simultaneously across multiple datasets, including automated transfer of the CellCover gene marker panels we have identified across cell types and developmental time (NeMO: Telley 3 CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=TelleyCellCover3sets&algo=binary), NeMO: Telley 12 CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=TelleyCellCover12sets&algo=binary), NeMO: Sorted Brain Cell CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=LiuCellCoverSorted&algo=binary), and NeMO: Blood 34 CellCover Panels (https://nemoanalytics.org/p?p=p&l=immune&c=CellCoverHao34&algo=binary)). CellCover is available in CellCover R (https://github.com/lanlanji/CoveringPackage) and CellCover Python (https://pypi.org/project/CellCover/).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 12 MeSH terms, 3 funders, 50 references.

Cite

This paper

Ji, L., Wang, A., Sonthalia, S., Seo, S., Naiman, D. Q., Younes, L., Colantuoni, C., & Geman, D. (2026). CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity. eLife, 14, RP107531. https://doi.org/10.7554/elife.107531

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