sartools_edger

sartools_edger performs differential gene expression analysis of RNA-Seq count data using edgeR-based methods to normalize counts, estimate dispersion, and test for differential expression.

Key Features:

• R-based implementation: Implements the pipeline in R and applies edgeR methodologies with incorporation of elements from DESeq2.
• Differential expression workflow: Performs normalization of count data, dispersion estimation, and statistical testing for differential expression.
• Quality control and diagnostic plots: Generates systematic quality-control checks and diagnostic plots to assess data quality and model assumptions.
• Reproducibility tracking: Records analysis parameters and R package versions to support reproducible computational results.
• Complex experimental designs: Handles multiple conditions of a biological factor and optional blocking factors such as batch effects or sample pairings.

Scientific Applications:

• Differential gene expression analysis: Identification of differentially expressed genes from RNA-Seq count data.
• RNA-Seq quality assessment: Evaluation of RNA-Seq data quality and model assumptions via diagnostic plots.
• Analysis of complex designs: Comparative analyses across multiple biological conditions including designs with batch effects or paired samples.
• High-throughput sequencing studies: Application to large-scale DNA/RNA sequencing datasets in biomedical research requiring statistical analysis of gene expression changes.

Methodology:

Performs count normalization, dispersion estimation, and statistical testing for differential expression using edgeR (with elements from DESeq2), generates quality-control diagnostic plots, and records analysis parameters and R package versions.