Pathway Overrepresentation
Calculate hypergeometric pathway over-representation and upper-tail probability.
Description
Pathway overrepresentation compares a selected-set overlap with a random draw from a caller-defined universe.
Pathway overrepresentation compares a selected-set overlap with a random draw from a caller-defined universe. The inclusive hypergeometric upper tail measures enrichment under that sampling model.1
Calculate hypergeometric pathway over-representation and upper-tail probability.
When to use Pathway Overrepresentation
Use this tool to calculate hypergeometric pathway over-representation and upper-tail probability.
Prepare the measured profile, formula counts, upstream model outputs or caller-defined reference criteria requested. Keep paired entries aligned and units, sample basis and preprocessing consistent.
How to use Pathway Overrepresentation and understand its results
- Prepare the measured profile, formula counts, upstream model outputs or caller-defined reference criteria requested. Keep paired entries aligned and units, sample basis and preprocessing consistent.
- Enter populationSize, pathwaySize, selectedSize, overlapSize using the field table and calculation rules, then run the tool.
- Read expectedOverlap, enrichmentFold, upperTailP. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| populationSize | Population size | number | Required |
| pathwaySize | Pathway size | number | Required |
| selectedSize | Selected size | number | Required |
| overlapSize | Overlap size | number | Required |
Enter safe integer universe, pathway, selected and overlap counts. Require 0<N≤100000, positive K,n≤N and feasible overlap. Sum upper-tail probabilities using log-factorials and cap p at one. Return expected overlap, enrichment and p.
expected=nK/N; enrichment=k/expected; p=P(X≥k) Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| expectedOverlap | Expected overlap | number |
| enrichmentFold | Enrichment fold | number |
| upperTailP | Upper tail p | number |
{
"input": {
"populationSize": 10,
"pathwaySize": 5,
"selectedSize": 2,
"overlapSize": 2
},
"output": {
"expectedOverlap": 1,
"enrichmentFold": 2,
"upperTailP": 0.22222222222222182
}
}Limitations and assumptions
Universe membership and pathway annotations are not fetched. No multiple-testing correction, dependence adjustment or biological validation is supplied.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Pathway Betweenness: Calculate undirected pathway graph betweenness by shortest-path dependency. For a complementary task, use Empirical Permutation P: Estimate permutation p-value with plus-one correction.
References
-
Pathway Overrepresentation — tool.io
Similar or alternative tools
- Pathway Betweenness
Calculate undirected pathway graph betweenness by shortest-path dependency.
- Empirical Permutation P
Estimate permutation p-value with plus-one correction.