GSEA Running Sum

Compute a weighted GSEA running sum and its strongest excursion.

Description

A weighted enrichment running sum rises at gene-set hits and falls at misses.

A weighted enrichment running sum rises at gene-set hits and falls at misses. Its largest absolute excursion summarizes concentration toward either end of an already ranked list.^[1]

Compute a weighted GSEA running sum and its strongest excursion.

When to use GSEA Running Sum

Use this tool to compute a weighted GSEA running sum and its strongest excursion.

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.

How to use GSEA Running Sum and understand its results

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
Enter rankedGeneIds, geneSetIds, weights using the field table and calculation rules, then run the tool.
Read runningScores, peakIndex, enrichmentScore. 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
rankedGeneIds	Ranked gene ids	list of string	Required
geneSetIds	Gene set ids	list of string	Required
weights	Weights	list of number	Required

Enter nonempty ranked IDs and gene-set IDs with aligned nonnegative weights. Require positive total hit weight and at least one miss. Scan in supplied order and return every running score, zero-based peak index and signed enrichment score. Equal absolute peaks keep the first.

hit step=weight/totalHitWeight; miss step=−1/missCount; ES=first strongest absolute excursion

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
runningScores	Running scores	list of number
peakIndex	Peak index	number
enrichmentScore	Enrichment score	number

json

{
  "input": {
    "rankedGeneIds": [
      "a",
      "b",
      "c",
      "d"
    ],
    "geneSetIds": [
      "a",
      "c"
    ],
    "weights": [
      1,
      1,
      1,
      1
    ]
  },
  "output": {
    "runningScores": [
      0.5,
      0,
      0.5,
      0
    ],
    "peakIndex": 0,
    "enrichmentScore": 0.5
  }
}

Limitations and assumptions

Ranking, weight selection, permutations and significance are upstream. Duplicate ranked IDs count as separate positions; this does not calculate normalized enrichment or filter a leading-edge hit subset.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Normalized Enrichment Score: Normalize enrichment against mean same-sign permutation magnitudes. For a complementary task, use Leading Edge Extractor: Extract the leading-edge subset around a running enrichment peak.

References

GSEA Running Sum — tool.io

Similar or alternative tools

Normalized Enrichment Score
Normalize enrichment against mean same-sign permutation magnitudes.
Leading Edge Extractor
Extract the leading-edge subset around a running enrichment peak.