Richard H. Scheuermann

The paper shows that differential expression methods like Wilcoxon rank-sum test produce unreliable cell-type markers due to cluster size dependence, and proposes a classification-based alternative with better specificity.

The paper extends the OMOP Common Data Model to support multimodal data integration across AMP projects by adding extension tables and a CDE Registry.

The paper constructs a multi-omics single-cell atlas of human RPE and choroid, identifying cell types, aging patterns, and genetic risk factors for AMD.

The paper creates a multi-omics atlas of human trabecular meshwork and ciliary body cells, identifying cell types and regulatory programs linked to glaucoma.

This paper introduces a standards framework for integrating single-cell and spatial omics data into brain cell atlases, enabling interoperable and reusable knowledge-ready resources.

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

The paper creates a manually annotated corpus of cell phenotype mentions and shows its use in improving named entity recognition and entity linking models.

This study shows that B cells in hepatitis C patients retain dysregulated transcriptional profiles after cure, with sustained TNF signaling driving chronic inflammation and loss of protective memory.

The paper creates a high-resolution multimodal lung atlas using single-nucleus RNA and chromatin data from 11 healthy adults, identifying 70 cell populations and 332,846 chromatin regions.

The paper benchmarks seven computational tools for matching cell types across cell atlases, finding variations in accuracy and proposing a framework for incremental growth of cell atlases.

NS-Forest v4.0 identifies optimal marker genes for cell type classification in scRNA-seq data, improving accuracy and selectivity for closely related cell types.

The paper analyzes the cochlear transcriptome of an outbred mouse population to identify cell-type specific changes associated with age-related hearing loss.

The paper introduces a data-driven method to extend cell ontologies for multi-modal single-cell omics data, focusing on the Primary Motor Cortex in humans, mice, and marmosets.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

FR-Match matches query single-cell RNA-sequencing datasets to reference atlases to identify common and novel cell types and suboptimally clustered cells with high accuracy.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

NS-Forest identifies minimal marker gene combinations for cell type determination from scRNA-seq data, revealing key roles of signaling and non-coding RNAs in neuronal identity.

The paper generates a genome and single nuclei transcriptome of Lemna minuta to trace all developmental and terminal cell types across the entire plant.

The paper compares immune responses to a Tdap booster in adults primed with acellular (aP) or whole-cell (wP) pertussis vaccines, identifying divergent gene expression and antibody subtype patterns in aP-primed individuals.

FR-Match matches cell type clusters from scRNA-seq data using the Friedman-Rafsky test, improving accuracy and identifying novel cell phenotypes.

The paper identifies von Economo neurons as a regionally specialized subtype of extratelencephalic-projecting excitatory neurons using single nucleus RNA-sequencing and cross-species comparisons.

The paper compares cell types in human and mouse cortex using single nucleus RNA-sequencing, finding conserved architecture with species-specific differences.

The paper shows that single-nucleus and single-cell RNA-seq can equally identify neuronal cell types in mouse visual cortex when intronic sequences are included.

This paper introduces a Bayesian method for automated cytometry data analysis that improves classification of cell types by incorporating prior knowledge and enabling cross-sample information sharing.

The paper identifies a novel human cortical GABAergic interneuron subtype with unique transcriptomic, morphological, and physiological features not observed in rodents.

The paper reviews how single cell RNA sequencing is identifying new human cell types and proposes a machine learning method to improve cell type definitions in the Cell Ontology.

DAFi identifies cell populations from flow cytometry data by combining recursive filtering and clustering with user-defined constraints, improving resolution of overlapping and rare populations.