Normalized Enrichment Score

Normalize enrichment against mean same-sign permutation magnitudes.

Description

Normalized enrichment compares observed enrichment with the magnitude of same-sign null scores.

Normalized enrichment compares observed enrichment with the magnitude of same-sign null scores. Positive and negative enrichment need separate null scaling.^[1]

Normalize enrichment against mean same-sign permutation magnitudes.

When to use Normalized Enrichment Score

Use this tool to normalize enrichment against mean same-sign permutation magnitudes.

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 Normalized Enrichment Score 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 observedEnrichment, nullScores using the field table and calculation rules, then run the tool.
Read nullMagnitudeMean, normalizedScore. 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
observedEnrichment	Observed enrichment	number	Required
nullScores	Null scores	list of number	Required

Supply a finite observed score and nonempty finite null list. Keep scores with exactly the same Math.sign value, average their absolute magnitudes and divide. No same-sign scores or zero mean magnitude is an error. A zero observed score cannot obtain a positive same-sign magnitude here.

NES=observedEnrichment/mean(|sameSignNullScores|)

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

Output field	Meaning	Value type
nullMagnitudeMean	Null magnitude mean	number
normalizedScore	Normalized score	number

json

{
  "input": {
    "observedEnrichment": 0.6,
    "nullScores": [
      0.2,
      -0.4,
      0.6
    ]
  },
  "output": {
    "nullMagnitudeMean": 0.4,
    "normalizedScore": 1.4999999999999998
  }
}

Limitations and assumptions

Permutations and enrichment scores must be generated upstream. This does not compute p-values, FDR or a null distribution, and sign matching excludes zeros for nonzero observed scores.

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 GSEA Running Sum: Compute a weighted GSEA running sum and its strongest excursion. For a complementary task, use Leading Edge Extractor: Extract the leading-edge subset around a running enrichment peak.

References

Normalized Enrichment Score — tool.io

Similar or alternative tools

GSEA Running Sum
Compute a weighted GSEA running sum and its strongest excursion.
Leading Edge Extractor
Extract the leading-edge subset around a running enrichment peak.