Shiny-Seq

Shiny-Seq performs integrated guided transcriptome analysis of RNA-Seq data, providing workflows for quality control, batch effect estimation and removal, enrichment analysis, and weighted gene co-expression network analysis (WGCNA).

Key Features:

• Guided Workflow: Stepwise guided pipeline for RNA-Seq data analysis.
• Batch Effect Estimation and Removal: Estimation and correction of batch effects to improve comparability across experimental conditions or batches.
• Quality Control and Visualization: Quality control checks with multiple visualization options to assess data integrity and preprocessing.
• Enrichment Analysis: Enrichment analysis using biological databases to identify significant pathways and processes associated with gene expression changes.
• Pattern Identification (WGCNA): Integration of weighted gene co-expression network analysis (WGCNA) to identify co-expression modules and network patterns.

Scientific Applications:

• Transcriptome analysis: Comprehensive analysis of RNA-Seq transcriptomes from quality assessment through network and enrichment analyses.
• Comparative studies: Support for comparative analyses across experimental conditions or batches through batch effect estimation and removal.
• Pathway and process identification: Identification of significant biological pathways and processes associated with differential gene expression via enrichment analysis against biological databases.
• Regulatory mechanism investigation: Discovery of co-expression modules and inference of complex regulatory mechanisms using WGCNA.

Methodology:

Implemented in R and leveraging the Shiny framework.