Canonical Pathway Analysis

Which curated pathways are over-represented in my selected genes? Canonical Pathway Analysis (CPA) connects a molecular signature to Reactome pathways, reports the features responsible for each match, and keeps the knowledge sources behind pathway membership visible.

How it works

1. Provide the signal. Upload the genes of interest and the background your assay could have detected. If the upload includes significance values, choose the cutoff for this run — a table carrying significance values is refused until a cutoff is declared. An upload without them is treated as already narrowed.

2. Test pathways. The inference finds candidate Reactome pathways and compares the signature overlap with each pathway's measured member set.

3. Explore the result. Review ranked pathways, statistical values, overlapping genes, available direction context, evidence, and a pathway network view.

Example input and output

Feature table

entity,effect,adj_p_value
PCNA,1.20,0.004
MCM2,1.08,0.006
MCM4,0.93,0.011
CDK1,1.31,0.003
CCNB1,1.15,0.005

Background and run settings

Assayed background: 17,980 genes
Significance column: adj_p_value
Significance cutoff: ≤ 0.05
Effect column: effect

Result

Pathway	Overlapping genes	Pathway size	q-value	Direction context
DNA Replication	PCNA, MCM2, MCM4	73 genes	0.002	Activated
Cell Cycle Checkpoints	CDK1, CCNB1, PCNA	112 genes	0.018	Inconclusive
Mitotic G1–G1/S Phases	CDK1, CCNB1	84 genes	0.047	not_computable

The first row means three selected genes occur together in a curated DNA-replication pathway more often than expected from the assayed background. The overlap members show exactly which submitted genes support that result.

Illustrative only — names and values above are simplified and fabricated to demonstrate the input and output shape. They are not a biological result.

What the result contains

• Pathway identity and name, together with the distinct genes counted in the test.

• p-value and q-value for enrichment relative to the run's measured background and tested pathway family.

• Overlap members showing which submitted genes support the pathway result.

• Direction context — Activated, Inhibited, or Inconclusive when the submitted effects and signed pathway relationships provide enough information; Biased when dataset skew rules out a reliable call; not_computable when there is not enough signed evidence to say anything.

• Knowledge sources and evidence status for the gene–pathway memberships used.

• A network artifact linking the selected genes to ranked pathways.

How statistical validity is protected

• The query is not the background. The test uses the selected signature as the query and a separate, experiment-appropriate background as the comparison population.

• Members live in gene space. Transcript and redundant isoform participation are projected carefully so one biological gene is not counted repeatedly simply because the knowledge graph stores several identifiers.

• Incomplete pathways are handled explicitly. A pathway with incomplete membership is named and withheld from correction, or the q-value is withheld for the family when completeness cannot be established.

Reading the result responsibly

• Pathway enrichment indicates that the signature is consistent with a curated pathway; it does not prove that the pathway caused the observed state.

• p-values and q-values are specific to the run's signature, measured background, knowledge-graph build, and pathway family. Avoid direct comparisons across unlike experiments.

• A pathway name does not by itself identify who asserted every membership. The result keeps assertion sources separate from the pathway's naming source.

• Direction fields require additional signed evidence and measured effects. A not_computable direction does not weaken the enrichment result, which can still be statistically valid on its own.

In short: CPA provides an evidence-linked pathway view of a molecular signature, with an explicit experimental background and guardrails against duplicated identifiers, incomplete membership, and misleading multiple-testing correction.

Next: Upstream Regulator Analysis.