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Donald Geman

As seen in: BioRxiv, Genome Research
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Articles

Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover

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By An Wang, Stephanie Hicks, Donald Geman, Laurent Younes
| Genome Research
1,6 1Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland 21218, USA; 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA; 3Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21218, USA; 4Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA; 5Malone Center for Engineering in Healthcare, Johns Hopkins University,...
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By An Wang, Stephanie Hicks, Donald Geman, Laurent Younes
| BioRxiv
Abstract The selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. Most current approaches to marker gene selection operate in a label-based framework, which is inherently limited by its dependency on predefined cell type labels or clustering results.
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GeneCover: A Combinatorial Approach for Label-free Marker Gene Selection

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By An Wang, Stephanie Hicks, Donald Geman, Laurent Younes
| BioRxiv
Abstract The selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. Most current approaches to marker gene selection operate in a label-based framework, which is inherently limited by its dependency on predefined cell type labels or clustering results.
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