TranscriptoScope

TranscriptoScope enables RNA-seq differential expression analysis, functional enrichment, ranked pathway analysis, and WGCNA coexpression-network analysis through established R and Bioconductor workflows.

Key Features:

• DESeq2 Differential Expression: Performs differential expression analysis using DESeq2 with support for additive and interaction designs.
• Normalized-Expression Testing: Conducts normalized-expression testing as part of the analytical workflow.
• Over-Representation Analysis: Performs over-representation analysis for functional enrichment of gene sets.
• fgsea-Ranked Pathway Analysis: Implements fast gene set enrichment analysis (fgsea) for ranked pathway analysis.
• WGCNA Coexpression-Network Analysis: Performs Weighted Gene Co-expression Network Analysis (WGCNA) to identify coexpression networks.
• Input Validation: Supports input validation for data quality assurance.
• Built-In Annotations: Includes built-in annotations for human, fruit-fly (Drosophila melanogaster), and yeast (Saccharomyces cerevisiae).
• Publication-Quality Plots: Generates publication-quality visualization outputs.
• Reproducibility Bundles: Produces reproducibility bundles containing results, settings, and executable R and R Markdown rerun code.

Scientific Applications:

• RNA-Seq Differential Expression Analysis: Identifies differentially expressed genes from RNA-seq data using DESeq2 with flexible experimental design matrices.
• Functional Enrichment Analysis: Determines over-represented functional categories among gene sets of interest.
• Ranked Pathway Analysis: Assesses pathway-level changes using ranked gene lists via fgsea methodology.
• Coexpression Network Analysis: Discovers gene modules and hub genes through WGCNA-based network construction.

Methodology:

TranscriptoScope implements established R and Bioconductor workflows including DESeq2 for differential expression, fgsea for ranked pathway analysis, and WGCNA for coexpression-network analysis.