Probabilistic Quotient

Normalize a metabolite profile by median sample/reference fold quotient.

Description

Probabilistic quotient normalization estimates a profile-wide scaling factor from feature-wise sample/reference ratios.

Probabilistic quotient normalization estimates a profile-wide scaling factor from feature-wise sample/reference ratios. A median limits the influence of some extreme ratios.^[1]

Normalize a metabolite profile by median sample/reference fold quotient.

When to use Probabilistic Quotient

Use this tool to normalize a metabolite profile by median sample/reference fold quotient.

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 Probabilistic Quotient 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 sampleSignals, referenceSignals using the field table and calculation rules, then run the tool.
Read quotient, normalizedSignals. 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
sampleSignals	Sample signals	list of number	Required
referenceSignals	Reference signals	list of number	Required

Enter nonempty aligned nonnegative sample signals and positive reference signals. Sort ratios to compute the median, averaging central values for even length; require positive median and return normalized signals in original order.

quotient=median(sample_i/reference_i); normalized_i=sample_i/quotient

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

Output field	Meaning	Value type
quotient	Quotient	number
normalizedSignals	Normalized signals	list of number

json

{
  "input": {
    "sampleSignals": [
      10,
      30,
      80
    ],
    "referenceSignals": [
      10,
      10,
      20
    ]
  },
  "output": {
    "quotient": 3,
    "normalizedSignals": [
      3.3333333333333335,
      10,
      26.666666666666668
    ]
  }
}

Limitations and assumptions

No reference profile is chosen or batch drift fitted. A zero median is rejected even if some sample signals are positive.

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 TIC Signal Normalization: Scale a peak-area vector to a supplied total ion current. For a complementary task, use Signal Drift Correction: Correct injection-order signal drift using aligned QC trend estimates.

References

Probabilistic Quotient — tool.io

Similar or alternative tools

TIC Signal Normalization
Scale a peak-area vector to a supplied total ion current.
Signal Drift Correction
Correct injection-order signal drift using aligned QC trend estimates.