Signal Drift Correction

Correct injection-order signal drift using aligned QC trend estimates.

Description

Signal-drift correction divides samples by aligned QC trend values relative to a reference.

Signal-drift correction divides samples by aligned QC trend values relative to a reference. The trend estimates must already represent the intended injection positions.^[1]

Correct injection-order signal drift using aligned QC trend estimates.

When to use Signal Drift Correction

Use this tool to correct injection-order signal drift using aligned QC trend estimates.

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 Signal Drift Correction 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, qcSignals, referenceQcSignal using the field table and calculation rules, then run the tool.
Read correctedSignals, correctionFactors. 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
qcSignals	Qc signals	list of number	Required
referenceQcSignal	Reference qc signal	number	Required

Enter nonempty aligned finite sample signals and positive QC/reference signals. Return factors and corrected signals in sample order; signed sample signals are accepted.

factor_i=referenceQC/QC_i; corrected_i=sample_i×factor_i

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

Output field	Meaning	Value type
correctedSignals	Corrected signals	list of number
correctionFactors	Correction factors	list of number

json

{
  "input": {
    "sampleSignals": [
      90,
      100,
      110
    ],
    "qcSignals": [
      90,
      100,
      110
    ],
    "referenceQcSignal": 100
  },
  "output": {
    "correctedSignals": [
      100,
      100,
      100
    ],
    "correctionFactors": [
      1.1111111111111112,
      1,
      0.9090909090909091
    ]
  }
}

Limitations and assumptions

The tool does not interpolate QC trends, inspect injection order or validate correction adequacy.

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 Probabilistic Quotient: Normalize a metabolite profile by median sample/reference fold quotient. For a complementary task, use Pooled QC Cv Gate: Gate pooled metabolomics QC by coefficient of variation.

References

Signal Drift Correction — tool.io

Similar or alternative tools

Probabilistic Quotient
Normalize a metabolite profile by median sample/reference fold quotient.
Pooled QC Cv Gate
Gate pooled metabolomics QC by coefficient of variation.