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Locat is a computational framework designed to identify highly specific marker genes in single-cell transcriptomics by jointly testing for expression concentration within compact cellular regions and significant depletion elsewhere. The method employs weighted Gaussian mixture models to generate a unified localization score, which was validated across synthetic benchmarks and diverse biological datasets including murine dermis, interferon-treated PBMCs, and embryonic stem cell differentiation. Results indicate that localized gene sets are often smaller than conventional selections while effectively preserving major cell population separation and developmental trajectories without requiring batch correction. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Several methods have been developed to identify marker genes that delineate cell populations in single-cell transcriptomic data, yet most emphasize enrichment within candidate populations without testing whether expression is significantly reduced outside those populations. We present Locat, a framework for identifying highly specific localized genes by testing whether expression is concentrated within compact regions of the cellular embedding and depleted elsewhere. For each gene, Locat fits weighted Gaussian mixture models to gene-specific and background densities, computes test statistics for concentration within compact regions and depletion outside those regions, and integrates the results into a unified localization score. Across synthetic benchmarks with controlled ground truth, Locat detects localized genes spanning uni-modal, multi-modal, and sparse expression patterns, and appropriately loses significance when simulated expression becomes indistinguishable from background structure. In biological datasets spanning developmental, perturbation, and differentiation contexts, Locat identifies compact marker sets that capture lineage organization, condition-specific programs, and temporal regulatory dynamics. Localized gene sets are often smaller than conventional feature selections such as highly variable genes, and embeddings constructed from localized gene sets tend to preserve separation of major cell populations and developmental programs. In murine dermis, embeddings computed using localized genes preserve differentiation and cell–cycle trajectories observed in the full dataset. In interferon– β –treated PBMCs, independent localization analysis of control and stimulated samples reveals stimulus-responsive programs and markers of shared immune populations without requiring batch correction or data integration. In retinoic acid–induced embryonic stem cell differentiation, localized genes exhibit reproducible stage-specific patterns across time points. Together, these results demonstrate that jointly assessing concentration and depletion yields specific, interpretable marker genes that enable direct cross-condition and multi-sample comparisons of marker genes across diverse biological settings.
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Abstract
Several methods have been developed to identify marker genes that delineate cell populations in single-cell transcriptomic data, yet most emphasize enrichment within candidate populations without testing whether expression is significantly reduced outside those populations. We present Locat, a framework for identifying highly specific localized genes by testing whether expression is concentrated within compact regions of the cellular embedding and depleted elsewhere. For each gene, Locat fits weighted Gaussian mixture models to gene-specific and background densities, computes test statistics for concentration within compact regions and depletion outside those regions, and integrates the results into a unified localization score.
Across synthetic benchmarks with controlled ground truth, Locat detects localized genes spanning uni-modal, multi-modal, and sparse expression patterns, and appropriately loses significance when simulated expression becomes indistinguishable from background structure. In biological datasets spanning developmental, perturbation, and differentiation contexts, Locat identifies compact marker sets that capture lineage organization, condition-specific programs, and temporal regulatory dynamics. Localized gene sets are often smaller than conventional feature selections such as highly variable genes, and embeddings constructed from localized gene sets tend to preserve separation of major cell populations and developmental programs. In murine dermis, embeddings computed using localized genes preserve differentiation and cell–cycle trajectories observed in the full dataset. In interferon–β–treated PBMCs, independent localization analysis of control and stimulated samples reveals stimulus-responsive programs and markers of shared immune populations without requiring batch correction or data integration. In retinoic acid–induced embryonic stem cell differentiation, localized genes exhibit reproducible stage-specific patterns across time points.
Together, these results demonstrate that jointly assessing concentration and depletion yields specific, interpretable marker genes that enable direct cross-condition and multi-sample comparisons of marker genes across diverse biological settings.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Funding information has been added to the acknowledgements section, and funding information in the biorXiv submission form has been updated.
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