Brian D. Aevermann

The paper describes a collaborative submission model for building high-quality single-cell genomics data resources, emphasizing partnership between contributors and curators to improve metadata quality and resource scalability.

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.

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.

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.