STAGEs

STAGEs performs static and temporal analysis of gene expression studies by integrating gene expression data processing, visualization, and pathway enrichment to identify differential expression patterns and implicated biological pathways.

Key Features:

• Excel data import: Accepts gene expression input from Excel spreadsheets for downstream analysis.
• Gene-to-date correction: Detects and corrects gene-to-date misconversions in Excel files to preserve accurate gene identifiers for analysis.
• Differential expression identification and visualization: Identifies and visualizes differentially expressed genes using volcano plots and stacked bar charts.
• Pathway enrichment analysis: Performs pathway enrichment using Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or user-supplied custom gene sets.
• Clustergrams: Generates clustergrams to display hierarchical clustering patterns within gene expression data.
• Correlation matrices: Computes and visualizes correlation matrices to examine sample- and gene-level relationships.
• Integrated analysis: Combines data processing, visualization, and pathway-level interpretation within a single analytical workflow.

Scientific Applications:

• Gene expression profiling: Characterizes static and temporal gene expression changes to identify differentially expressed genes across conditions or time points.
• Pathway and mechanism analysis: Interprets gene expression changes at the pathway level using Enrichr and GSEA to investigate biological processes and disease mechanisms.
• Pattern discovery and hypothesis generation: Uses clustergrams and correlation matrices to reveal expression patterns and relationships that support hypothesis generation and validation from omics datasets.

Methodology:

Integration of data processing and visualization (volcano plots, stacked bar charts, clustergrams, correlation matrices), correction of Excel gene-to-date misconversions, and pathway enrichment using Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or custom gene sets.