GeneMarkeR

GeneMarkeR identifies standardized marker genes from single-cell RNA sequencing (scRNA-seq) datasets to enable accurate assessment of cell type specificity across species, methodologies, and sample types.

Key Features:

• Standardization algorithm: Implements a novel algorithm to standardize marker gene identification across studies and methodologies.
• Standardized public data retrieval: Retrieves and standardizes public scRNA-seq data supporting marker genes for consistent downstream analysis.
• Reactive programming: Employs reactive programming for dynamic data retrieval and updates.
• Comprehensive database: Hosts over 261,000 rows of standardized marker gene results from 25 studies, encompassing 21,012 unique genomic entities and 99 unique cell types.
• Hierarchical ontology mapping: Maps marker gene entries to hierarchical ontologies to represent cellular relationships and hierarchies.
• Cross-species and methodological flexibility: Supports identification and comparison of marker genes across different species and experimental methodologies.
• MySQL-backed storage: Stores standardized marker results in a MySQL database infrastructure.

Scientific Applications:

• Cell type classification: Enables accurate classification of cell types in scRNA-seq studies using standardized marker genes.
• Comparative marker analysis: Facilitates comparative analysis of marker genes across species and experimental methods.
• Cellular heterogeneity assessment: Supports characterization of cellular heterogeneity and subtype-specific marker identification.
• Ontology-driven analyses: Allows hierarchical analyses of cell types via ontology mappings.
• Database standardization and updating: Provides a framework for integrating and standardizing new marker gene data as they become available.

Methodology:

Applies a novel standardization algorithm to standardized public scRNA-seq marker data, maps results to hierarchical ontologies, stores outputs in a MySQL database, and enables dynamic retrieval via reactive programming.