Step 1 · Load Dataset
Upload a TSV/CSV/Excel file exported from the IVCCA MATLAB tool. Optionally include a plain-text gene filter list (one symbol per line).
Step 2 · Correlation Matrix
Choose a correlation method and compute the IVCCA correlation matrix to unlock downstream visualisation and clustering tools.
Step 3 · Clustering & Dimensionality Reduction
Discover structure in your correlation matrix using K-means, PCA, and t-SNE projections.
Step 5 · Advanced Analysis
Gene-to-gene, gene-to-pathway, multi-pathway, Venn diagram, and network analysis.
Single Pathway Analysis
Analyze correlations within a single pathway (set of genes).
Gene to Genes
Calculate correlation between a single gene and a group of target genes.
Gene to Pathways
Calculate correlation between a single gene and multiple pathways.
Multi-Pathway Analysis (CECI)
Calculate CECI (Cross-Enrichment Correlation Index) for multiple pathways.
Compare Pathways
Calculate cosine similarity between two pathways.
Venn Diagram
Generate Venn diagram for two pathways.
Network Analysis
Generate 2D or 3D network graph based on correlation threshold.
Session Status
Start by loading your IVCCA dataset (.xlsx, .csv, or .tsv). Follow the numbered workflow to reproduce the MATLAB pipeline.