DiffExpress

DiffExpress performs differential expression analysis of RNA-seq data to identify gene expression differences in transcriptomics studies while modeling complex sources of variation across samples.

Key Features:

• Differential expression analysis (DEA): Implements differential expression analysis specifically for RNA-seq transcriptomics data.
• edgeR integration: Leverages the edgeR package for statistical testing of differential expression.
• Statistical model validation: Provides bespoke statistical model validation to guide necessary adjustments to datasets or models.
• Variance modeling and scalability: Processes large datasets and models complex sources of variation across samples.
• Reproducibility and data validation: Emphasizes reproducibility through organized results and data validation procedures.

Scientific Applications:

• Transcriptomics differential expression: Detects gene expression differences across experimental conditions or treatments using RNA-seq data.
• Biological hypothesis testing: Supports statistical hypothesis testing of gene-level expression changes in transcriptomics studies.

Methodology:

Uses the edgeR package for differential expression testing, a data-driven workflow with built-in statistical model validation, and computational procedures for processing large RNA-seq datasets and modeling complex sources of variation across samples.