Understand the analysis, not just the picture.

Module 01 · Foundations

Sets, unions and intersections

A set is a collection of distinct elements. An element can be a gene symbol, accession, sample identifier, category or any other comparable value.

Worked example. If A = {A, B, C, D} and B = {C, D, E}, then A ∩ B = {C, D} and A ∪ B = {A, B, C, D, E}.

Module 02 · Representation

Venn for small comparisons; UpSet for larger ones

Venn diagrams are intuitive for two or three sets because all membership regions can be shown compactly. For larger comparisons, UpSet represents intersection combinations with a membership matrix and bars, avoiding increasingly complex circle geometry.

Exact A ∩ B excludes elements that are also in another displayed set. Inclusive A ∩ B includes them.

Module 03 · Similarity

Jaccard, Dice and overlap coefficient

Jaccard = |A ∩ B| / |A ∪ B|. Dice = 2|A ∩ B| / (|A| + |B|). Overlap coefficient = |A ∩ B| / min(|A|, |B|). A high overlap coefficient can coexist with a lower Jaccard value when a small set is largely contained in a much larger set.

Module 04 · Statistical overlap

A p-value needs a background universe.

Overlap enrichment asks whether the observed co-membership is unusual relative to a defined population of possible elements. For omics experiments, the appropriate universe is often the genes or proteins that could realistically have been detected or tested, not automatically every identifier in a reference database.

VennScope can report hypergeometric enrichment, two-sided Fisher testing, odds ratios and Benjamini–Hochberg-adjusted q-values for pairwise overlap analyses. Ordinary identifier lists are membership data, not replicate continuous measurements; a t-test is therefore not applied to plain set lists.

Module 05 · Biological identifiers

Gene, transcript and protein IDs are related, not interchangeable.

A gene can have multiple transcripts and protein products. Mappings may be one-to-one, one-to-many, many-to-one or unavailable. VennScope therefore keeps raw comparison literal, makes mapping explicit and preserves original identifiers alongside derived mapped datasets.

Learn identifier sources →

Module 06 · Omics workflow

Where VennScope fits in genomics, transcriptomics and proteomics

A common workflow is: derive biologically justified identifier lists from an upstream experiment → compare sets and inspect intersections in VennScope → export selected intersections → perform downstream functional interpretation in an appropriate enrichment/pathway tool. VennScope does not claim a Gene Ontology enrichment module in this beta.

Module 07 · Guided tutorials

Practice with synthetic data

Reference principle

Keep source provenance with consequential analyses.

Database mappings and annotations change. Keep mapping outputs and access dates with your analysis, and consult the primary reference services listed on the Identifier sources page.