AbsoluteQuantificationChecker

Absolute Quantification

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: Absolute Quantification

Absolute Quantification Checker — Absolute Quantification in Liquid Biopsy

ℹ️ Utility performs processing and validation of absolute quantitative analysis data (ddPCR/qPCR) for liquid biopsy samples according to MIQE guidelines:
• Precise Concentration Calculation: Conversion of raw instrument data to copies/mL considering all dilutions.
• Reaction Quality Control: Verification of successful partition count (droplets) for statistical reliability.
• Threshold Values: Classification of results into Quantified, Detected Below LoQ, and Not Detected.
• MRD Monitoring: Support for low detection limits critical for minimal residual disease identification.

⚠️ IMPORTANT:
• In digital PCR, result depends on correct droplet gating.
• Results below LoQ have high uncertainty and are not recommended for therapy response kinetics calculation.

Usage:
AbsoluteQuantificationChecker.exe → demo mode (console output)
AbsoluteQuantificationChecker.exe input.csv output.json → evaluate your data

Input format:
SampleID,TargetGene,PositiveDroplets,TotalDroplets,Concentration_Copies_Per_uL,DilutionFactor,InputVolume_uL,LoQ_Copies_Per_mL

Example:
LB-001,EGFR,45,18500,2.5,1,20,100

📍 Scope of Application (Usage Where):
• Oncology: Monitoring mutations (EGFR, KRAS, BRAF) in circulating tumor DNA (ctDNA).
• Virology: Precise viral load counting (HBV, HCV, HIV).
• Gene Therapy: Vector copy number control.
• R&D: Validation of new biomarkers.

— WHY IS THIS NEEDED?
Relative methods (qPCR Cq) depend on amplification efficiency and standards.
Absolute quantification (ddPCR) does not require calibration curve and provides highest precision at low concentrations.
This is critically important for making clinical decisions on therapy switching upon disease progression.

⚠️ CRITICAL:
• Droplet Count: Less than 10,000 droplets reduces sensitivity and Poisson accuracy.
• Rain: Presence of "rain" (intermediate droplets) can distort gating results.
• LoD vs LoQ: LoD is presence fact, LoQ is precise quantity. Do not confuse them.
• Inhibitors: PCR inhibitors in plasma may falsely lower positive droplet count.

Key features:
• Automatic unit conversion (copies/uL -> copies/mL)
• Strict statistical significance check (droplet count)
• Graded status system (Quantified/Detected/Not Detected)
• Support for complex dilution schemes
• Compliance with MIQE standards for data publication

Critical parameters:
• Total Droplets: ≥ 10,000
• Final Concentration: Comparison with LoQ/LoD
• Positive Fraction: Fraction of positive events

💡 Usage tips:
1. Gating: Carefully adjust amplitude thresholds to separate positive and negative droplets.
2. Controls: Always include No Template Control (NTC) for background assessment.
3. Input Volume: Use maximum possible plasma volume to increase sensitivity.
4. Documentation: Save 2D droplet distribution plots as part of raw data.
5. Validation: Regularly verify LoD and LoQ for each new primer panel.

⚠️ Note: This utility is a bioinformatic processing tool for raw PCR data. It does not replace clinical interpretation by oncologist but provides precise quantitative data for decision making.

input.csv

SampleID,TargetGene,PositiveDroplets,TotalDroplets,Concentration_Copies_Per_uL,DilutionFactor,InputVolume_uL,LoQ_Copies_Per_mL
LB-2026-PT-001,EGFR L858R,45,18500,2.5,1,20,100
LB-2026-PT-002,KRAS G12D,2,19000,0.1,1,20,100
LB-2026-PT-003,BRAF V600E,120,17800,6.8,2,10,50

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of AbsoluteQuantificationChecker for Absolute Quantification Checker.

Domain limits URS input.csv FS IQ/OQ/PQ

Domain limits and critical parameters

Key fragments from the source description are shown below. Before production use, limits must be verified against the approved specification, registration dossier and local SOPs.

• MRD Monitoring: Support for low detection limits critical for minimal residual disease identification.
This is critically important for making clinical decisions on therapy switching upon disease progression.
⚠️ CRITICAL:
• Automatic unit conversion (copies/uL -> copies/mL)
Critical parameters:
• Total Droplets: ≥ 10,000
• Final Concentration: Comparison with LoQ/LoD

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for Absolute Quantification Checker with headers defined in the data contract.	High	The file is processed without manual header editing.
URS-002	The utility shall perform deterministic QC evaluation without machine learning and without probabilistic conformance decisions.	High	Identical input data, rule version and configuration produce reproducible results.
URS-003	The utility shall validate mandatory fields, data types, ranges, units and domain plausibility.	High	Schema, conversion and range errors are explicitly reported.
URS-004	The utility shall apply domain limits and rules from the description, approved specification, registration dossier and local SOPs.	High	Each check has PASS/WARNING/FAIL and a clear message.
URS-005	The utility shall generate output.json with machine-readable results, source values, warnings, failures and critical findings.	High	JSON is suitable for LIMS/ELN/MES integration and QA/QC review.
URS-006	The utility shall preserve traceability between batch/sample, input file, applied rules and final status.	High	Output contains identifiers, checked parameters and audit metadata.
URS-007	The documentation shall support IQ/OQ/PQ, CSV/CSA and review by internal QA or inspectors.	Medium	URS, FS, input/output contract and test scenarios are supplied with the utility.
URS-008	The utility shall be used as a QC decision-support tool and not as a substitute for approved specifications and QA/QP release decision.	Medium	Documentation states change control and limit-verification expectations.

input.csv contract

#	Field	Type	Sample	Purpose
1	`SampleID`	string / controlled vocabulary	`LB-2026-PT-001`	Sample or laboratory specimen identifier.
2	`TargetGene`	string / controlled vocabulary	`EGFR L858R`	Controlled input parameter for deterministic QC rules.
3	`PositiveDroplets`	decimal	`45`	Controlled input parameter for deterministic QC rules.
4	`TotalDroplets`	decimal	`18500`	Controlled input parameter for deterministic QC rules.
5	`Concentration_Copies_Per_uL`	decimal	`2.5`	Component ratio; structural or formulation CQA.
6	`DilutionFactor`	decimal	`1`	Controlled input parameter for deterministic QC rules.
7	`InputVolume_uL`	decimal	`20`	Volume/dose used for load, limit or release calculation.
8	`LoQ_Copies_Per_mL`	decimal	`100`	Volume/dose used for load, limit or release calculation.

SampleID,TargetGene,PositiveDroplets,TotalDroplets,Concentration_Copies_Per_uL,DilutionFactor,InputVolume_uL,LoQ_Copies_Per_mL
LB-2026-PT-001,EGFR L858R,45,18500,2.5,1,20,100
LB-2026-PT-002,KRAS G12D,2,19000,0.1,1,20,100
LB-2026-PT-003,BRAF V600E,120,17800,6.8,2,10,50

Input validation rules

ID	Field	Rule	Criticality
VR-001	`SampleID`	The field shall match an approved dictionary or accepted string representation.	High
VR-002	`TargetGene`	The field shall match an approved dictionary or accepted string representation.	High
VR-003	`PositiveDroplets`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`TotalDroplets`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`Concentration_Copies_Per_uL`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`DilutionFactor`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`InputVolume_uL`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`LoQ_Copies_Per_mL`	The field shall match an approved dictionary or accepted string representation.	Medium

FS — Functional Specification

ID	Function	Implementation
FS-001	CLI execution	Support execution modes: demo mode without arguments and production mode input.csv output.json.
FS-002	CSV import	Read input.csv in UTF-8/CSV-compatible format and validate header and expected columns.
FS-003	Schema validation	Check mandatory fields, column count, unknown key fields and empty mandatory values.
FS-004	Type conversion	Convert numeric, flag and text values; invalid format is recorded as a row-level error.
FS-005	Domain rule engine	Apply rules for Absolute Quantification Checker, including critical limits from the description and approved specification.
FS-006	Status aggregation	Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance.
FS-007	JSON export	Write output.json with detailed checks, source values, warnings, failures and critical findings.
FS-008	Audit support	Keep result structure suitable for review, deviation investigation and calculation reproduction.
FS-009	Integration contract	Support the scenario LIMS/ELN/MES → input.csv → utility → output.json → portal/admin review.
FS-010	Error handling	Return explicit messages for missing file, empty CSV, invalid schema, output write failure and invalid format.

Example output.json

{
  "utilityId": "absolutequantificationchecker",
  "utilityFolder": "AbsoluteQuantificationChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "SampleID",
      "value": "LB-2026-PT-001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "TargetGene",
      "value": "EGFR L858R",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "PositiveDroplets",
      "value": "45",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "TotalDroplets",
      "value": "18500",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "Concentration_Copies_Per_uL",
      "value": "2.5",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "DilutionFactor",
      "value": "1",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "InputVolume_uL",
      "value": "20",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "LoQ_Copies_Per_mL",
      "value": "100",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    }
  ],
  "criticalFindings": [],
  "warnings": [],
  "audit": {
    "inputHash": "sha256:<calculated at runtime>",
    "rulesVersion": "<utility executable version>",
    "documentation": "AbsoluteQuantificationChecker.documentation.html"
  }
}

Traceability matrix

URS	FS	Test	Evidence
URS-001	FS-001, FS-002	OQ-001	Verify execution and import of valid input.csv.
URS-002	FS-005, FS-006	OQ-004	Repeat the same dataset and compare output.json.
URS-003	FS-003, FS-004, FS-010	OQ-002, OQ-003	Verify missing columns and invalid types.
URS-004	FS-005, FS-006	OQ-004, PQ-001	Verify critical deviations on real/boundary data.
URS-005	FS-007, FS-009	OQ-005	Verify JSON schema and downstream-system suitability.
URS-006	FS-008	OQ-006	Verify identifiers and audit metadata.
URS-007	FS-008, FS-010	IQ-001, OQ-007	Verify documentation completeness and control evidence.
URS-008	FS-005, FS-008	PQ-002	Verify review workflow and no replacement of QA decision.

IQ/OQ/PQ test scenarios

ID	Scenario	Expected result
IQ-001	Verify executable, input.csv, documentation and checksum availability.	Delivery set is complete; version is recorded.
OQ-001	Valid sample row from input.csv.	PASS or acceptable WARNING according to rules.
OQ-002	Remove a mandatory CSV column.	Schema error or FAIL with missing-column reference.
OQ-003	Place a non-numeric value into a numeric field.	Type-conversion error with row/field reference.
OQ-004	Set a critical parameter outside the limit.	FAIL and critical finding.
OQ-005	Verify output.json structure.	All mandatory sections are present and JSON is valid.
OQ-006	Verify batch/sample traceability.	Input and result identifiers match.
PQ-001	Verify 3–5 real user batches/samples.	Result is confirmed by QC/QA review.
PQ-002	Verify deviation workflow and manual QA decision.	Utility supports review but does not replace approved decision.

QA/QC and change control

Do not rename columns without updating validator, documentation and test set.
Retain input.csv, output.json, executable version and checksum.
Before production use, perform IQ/OQ/PQ or equivalent CSV/CSA verification.
Critical limits shall be verified against the approved specification, registration dossier and local SOPs.
The utility provides structured QC decision support; final release decision remains with QA/QP and approved procedures.

Included in packages

Liquid Biopsy QC Suite

QC and pre-analytical control package for liquid biopsy workflows: cfDNA/ctDNA, CTC, EV/exosomes, methylation, NGS/qPCR/ddPCR, sample quality, contamination, sensitivity and reporting checks.

Open