Effect Direction Classifier

Classify metabolite fold-change direction without significance claims.

Description

Effect-direction classification applies a minimum absolute magnitude to already computed signed effects.

Effect-direction classification applies a minimum absolute magnitude to already computed signed effects. The gate distinguishes magnitude from statistical significance.^[1]

Classify metabolite fold-change direction without significance claims.

When to use Effect Direction Classifier

Use this tool to classify metabolite fold-change direction without significance claims.

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 Effect Direction Classifier 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 log2FoldChanges, minimumMagnitude using the field table and calculation rules, then run the tool.
Read directions, changedCount. 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
log2FoldChanges	Log 2 fold changes	list of number	Required
minimumMagnitude	Minimum magnitude	number	Required

Enter finite effects and nonnegative minimum. Empty lists are allowed. Return direction labels and changed count; equality at a positive threshold counts as changed.

unchanged if |effect|<minimum; otherwise up/down by sign, with zero unchanged

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

Output field	Meaning	Value type
directions	Directions	list of string
changedCount	Changed count	number

json

{
  "input": {
    "log2FoldChanges": [
      -2,
      0.5,
      2
    ],
    "minimumMagnitude": 1
  },
  "output": {
    "directions": [
      "down",
      "unchanged",
      "up"
    ],
    "changedCount": 2
  }
}

Limitations and assumptions

No effect estimation, uncertainty or significance testing is provided.

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 VIP Threshold Gate: Gate variable-importance scores against a supplied VIP minimum. For a complementary task, use Empirical Permutation P: Estimate permutation p-value with plus-one correction.

References

Effect Direction Classifier — tool.io

Similar or alternative tools

VIP Threshold Gate
Gate variable-importance scores against a supplied VIP minimum.
Empirical Permutation P
Estimate permutation p-value with plus-one correction.