ORdensity

ORdensity identifies differentially expressed genes (DEGs) from microarray gene expression datasets using outlier detection methods described by Irigoien and Arenas (BMC Bioinformatics, 2018) to select informative genes.

Key Features:

• Statistical Methodology: Employs outlier detection-based statistical methods from Irigoien and Arenas (BMC Bioinformatics, 2018) to identify genes with significant differential expression.
• R package implementation: Provided as an R package for performing the analysis within the R environment.
• Parallel implementation: Supports parallel execution to improve run-time efficiency and optimize memory usage for large datasets.
• Validation and robustness: Demonstrated robustness on both simulated and real datasets across various data types.
• Microarray applicability: Applied specifically to gene expression data produced by microarray technology.

Scientific Applications:

• DEG discovery: Extraction of biologically informative differentially expressed genes from microarray expression datasets.
• Disease mechanism studies: Identification of genes implicated in disease-related expression changes to inform mechanistic research.
• Biomarker discovery: Selection of candidate biomarkers based on outlier-driven differential expression signals.
• Personalized medicine: Supporting identification of expression signatures relevant to individualized treatment approaches.
• High-throughput genomic studies: Scalability to large-scale gene expression experiments typical of high-throughput studies.

Methodology:

Applies statistical outlier detection to gene expression measurements as described by Irigoien and Arenas (BMC Bioinformatics, 2018); implemented as an R package with a parallel execution option and validated on simulated and real datasets.