GenomeWideSignalBatchChecker

Genome-Wide Signal Batch

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: Genome-Wide Signal Batch

Genome-Wide Signal Batch Checker — Batch Effect Control in Genome-Wide Data

ℹ️ Utility performs systematic batch effect assessment according to ENCODE, IHEC standards and reproducibility guidelines:
• PCA-Batch Correlation: Coefficient of determination (R²) between principal components and batch variable.
• Variance Decomposition: Ratio of inter-batch to intra-batch variance as measure of technical noise dominance.
• Outlier Detection: Identification of samples deviating from their batch or forming separate clusters.
• Confounding Check: Detection of situations where batch fully or partially overlaps with biological variable.
• Actionable Classification: NO_BATCH_EFFECT / MINOR_EFFECT / SEVERE_EFFECT with specific recommendations.

⚠️ IMPORTANT:
• PC1-Batch R² > 0.15 indicates significant batch effect requiring correction.
• Batch-phenotype confounding CANNOT be corrected statistically — experiment redesign required.
• Inter-batch variance >20% means technical variation exceeds biological variation.
• Batch effect correction (ComBat, SVA, RUV) should be applied ONLY after effect confirmation.

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

Input format:
StudyID,DataType,BatchID,SampleCount_InBatch,PC1_VarianceExplained_Percent,PC2_VarianceExplained_Percent,PC1_BatchCorrelation_R2,MaxAcceptable_PC1_BatchR2,InterBatchVariance_Percent,MaxInterBatchVariance_Percent,IntraBatchCV_Percent,OutlierSampleCount,TotalSamplesInStudy,BatchConfoundedWithPhenotype,ProcessingDate,InstrumentID,OperatorID_Source

Example:
GWAS-01,SNPArray,BATCH-A,120,8.2,3.1,0.04,0.15,6.5,20,4.2,2,360,false,2026-01-15,Illumina-iScan,OP-01

📍 Scope of Application (Usage Where):
• GWAS/EWAS: Mandatory QC before association analysis.
• Multi-Center Studies: Harmonization of data from different sources.
• Epigenomic Consortia (IHEC, ENCODE): Meta-analysis standardization.
• Data Publication: Requirement of most journals for genome-wide studies.

— WHY IS THIS NEEDED?
Batch effect is the most frequent cause of irreproducibility in genomics.
False associations caused by technical artifacts are published annually and undermine trust in science.
Automated verification allows problem detection BEFORE expensive analysis and publication.
This is fundamental scientific integrity and patient safety requirement in translational research.

⚠️ CRITICAL:
• PC1-Batch R² ≤ 0.15: Significance threshold for most genome-wide data.
• Inter-Batch Variance ≤ 20%: Exceedance = technical noise dominates.
• Confounding = SEVERE: Statistical correction impossible without biological signal loss.
• Outliers < 5%: High outlier percentage indicates systemic batch issue.
• Documentation: ALL batch variables must be recorded in metadata.

Key features:
• Five-parameter batch effect assessment
• Automatic confounding detection
• Study-level aggregation (not per individual batch)
• Classification with actionable recommendations
• Compliance with ENCODE/IHEC standards

Critical parameters:
• PC1-Batch R²: ≤ 0.15
• Inter-Batch Variance: ≤ 20%
• Outlier Rate: < 5%
• Batch-Phenotype Confounding: Absent
• Number of Batches: > 1

💡 Usage tips:
1. Experimental Design: Randomize samples across batches; never group cases/controls into separate batches.
2. Metadata: Record ALL technical variables (date, instrument, operator, reagent lot).
3. Reference Samples: Include common control samples in each batch for direct drift assessment.
4. Correction: Apply ComBat/SVA only upon confirmed effect; always verify correction result visually.
5. Publication: Provide PCA plots before and after correction as supplementary material.

⚠️ Note: This utility assesses presence and severity of batch effect. It does not perform correction itself — use specialized tools (ComBat, sva, RUVSeq) for that. The utility determines WHEN and WHICH correction to apply.

input.csv

StudyID,DataType,BatchID,SampleCount_InBatch,PC1_VarianceExplained_Percent,PC2_VarianceExplained_Percent,PC1_BatchCorrelation_R2,MaxAcceptable_PC1_BatchR2,InterBatchVariance_Percent,MaxInterBatchVariance_Percent,IntraBatchCV_Percent,OutlierSampleCount,TotalSamplesInStudy,BatchConfoundedWithPhenotype,ProcessingDate,InstrumentID,OperatorID_Source
GWAS-2026-01,SNPArray,BATCH-A,120,8.2,3.1,0.04,0.15,6.5,20.0,4.2,2,360,false,2026-01-15,Illumina-iScan,OP-01
GWAS-2026-01,SNPArray,BATCH-B,120,8.2,3.1,0.05,0.15,6.5,20.0,3.8,1,360,false,2026-02-10,Illumina-iScan,OP-02
GWAS-2026-01,SNPArray,BATCH-C,120,8.2,3.1,0.03,0.15,6.5,20.0,4.5,3,360,false,2026-03-05,Illumina-iScan,OP-01
METHYL-2026-02,MethylationArray,BATCH-X,48,35.6,12.4,0.68,0.15,42.0,20.0,8.5,7,96,true,2026-01-20,Illumina-iScan,OP-03
METHYL-2026-02,MethylationArray,BATCH-Y,48,35.6,12.4,0.72,0.15,42.0,20.0,9.1,5,96,true,2026-04-01,Illumina-iScan,OP-04

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of GenomeWideSignalBatchChecker for Genome-Wide Signal Batch 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.

• PC1-Batch R² > 0.15 indicates significant batch effect requiring correction.
• Inter-batch variance >20% means technical variation exceeds biological variation.
⚠️ CRITICAL:
• PC1-Batch R² ≤ 0.15: Significance threshold for most genome-wide data.
• Inter-Batch Variance ≤ 20%: Exceedance = technical noise dominates.
• Outliers < 5%: High outlier percentage indicates systemic batch issue.
• Documentation: ALL batch variables must be recorded in metadata.
Critical parameters:
• PC1-Batch R²: ≤ 0.15
• Inter-Batch Variance: ≤ 20%
• Outlier Rate: < 5%
• Number of Batches: > 1
⚠️ Note: This utility assesses presence and severity of batch effect. It does not perform correction itself — use specialized tools (ComBat, sva, RUVSeq) for that. The utility determines WHEN and WHICH correction to apply.

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for Genome-Wide Signal Batch 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	`StudyID`	string / controlled vocabulary	`GWAS-2026-01`	Controlled input parameter for deterministic QC rules.
2	`DataType`	string / controlled vocabulary	`SNPArray`	Controlled input parameter for deterministic QC rules.
3	`BatchID`	string / controlled vocabulary	`BATCH-A`	Batch or lot identifier used for traceability.
4	`SampleCount_InBatch`	integer / decimal	`120`	Batch or lot identifier used for traceability.
5	`PC1_VarianceExplained_Percent`	decimal	`8.2`	Controlled input parameter for deterministic QC rules.
6	`PC2_VarianceExplained_Percent`	decimal	`3.1`	Controlled input parameter for deterministic QC rules.
7	`PC1_BatchCorrelation_R2`	string / controlled vocabulary	`0.04`	Batch or lot identifier used for traceability.
8	`MaxAcceptable_PC1_BatchR2`	string / controlled vocabulary	`0.15`	Batch or lot identifier used for traceability.
9	`InterBatchVariance_Percent`	decimal	`6.5`	Batch or lot identifier used for traceability.
10	`MaxInterBatchVariance_Percent`	decimal	`20.0`	Batch or lot identifier used for traceability.
11	`IntraBatchCV_Percent`	decimal	`4.2`	Batch or lot identifier used for traceability.
12	`OutlierSampleCount`	integer / decimal	`2`	Sample or laboratory specimen identifier.
13	`TotalSamplesInStudy`	string / controlled vocabulary	`360`	Sample or laboratory specimen identifier.
14	`BatchConfoundedWithPhenotype`	string / controlled vocabulary	`false`	Batch or lot identifier used for traceability.
15	`ProcessingDate`	string / controlled vocabulary	`2026-01-15`	Controlled input parameter for deterministic QC rules.
16	`InstrumentID`	string / controlled vocabulary	`Illumina-iScan`	Controlled input parameter for deterministic QC rules.
17	`OperatorID_Source`	string / controlled vocabulary	`OP-01`	Operator/analyst identifier for audit trail context.

StudyID,DataType,BatchID,SampleCount_InBatch,PC1_VarianceExplained_Percent,PC2_VarianceExplained_Percent,PC1_BatchCorrelation_R2,MaxAcceptable_PC1_BatchR2,InterBatchVariance_Percent,MaxInterBatchVariance_Percent,IntraBatchCV_Percent,OutlierSampleCount,TotalSamplesInStudy,BatchConfoundedWithPhenotype,ProcessingDate,InstrumentID,OperatorID_Source
GWAS-2026-01,SNPArray,BATCH-A,120,8.2,3.1,0.04,0.15,6.5,20.0,4.2,2,360,false,2026-01-15,Illumina-iScan,OP-01
GWAS-2026-01,SNPArray,BATCH-B,120,8.2,3.1,0.05,0.15,6.5,20.0,3.8,1,360,false,2026-02-10,Illumina-iScan,OP-02
GWAS-2026-01,SNPArray,BATCH-C,120,8.2,3.1,0.03,0.15,6.5,20.0,4.5,3,360,false,2026-03-05,Illumina-iScan,OP-01

Input validation rules

ID	Field	Rule	Criticality
VR-001	`StudyID`	The field shall match an approved dictionary or accepted string representation.	High
VR-002	`DataType`	The field shall match an approved dictionary or accepted string representation.	High
VR-003	`BatchID`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`SampleCount_InBatch`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`PC1_VarianceExplained_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`PC2_VarianceExplained_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`PC1_BatchCorrelation_R2`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`MaxAcceptable_PC1_BatchR2`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-009	`InterBatchVariance_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-010	`MaxInterBatchVariance_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-011	`IntraBatchCV_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-012	`OutlierSampleCount`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-013	`TotalSamplesInStudy`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-014	`BatchConfoundedWithPhenotype`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-015	`ProcessingDate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-016	`InstrumentID`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-017	`OperatorID_Source`	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 Genome-Wide Signal Batch 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": "genomewidesignalbatchchecker",
  "utilityFolder": "GenomeWideSignalBatchChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "StudyID",
      "value": "GWAS-2026-01",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "DataType",
      "value": "SNPArray",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "BatchID",
      "value": "BATCH-A",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "SampleCount_InBatch",
      "value": "120",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "PC1_VarianceExplained_Percent",
      "value": "8.2",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "PC2_VarianceExplained_Percent",
      "value": "3.1",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "PC1_BatchCorrelation_R2",
      "value": "0.04",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "MaxAcceptable_PC1_BatchR2",
      "value": "0.15",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    },
    {
      "parameter": "InterBatchVariance_Percent",
      "value": "6.5",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-009"
    },
    {
      "parameter": "MaxInterBatchVariance_Percent",
      "value": "20.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-010"
    },
    {
      "parameter": "IntraBatchCV_Percent",
      "value": "4.2",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-011"
    },
    {
      "parameter": "OutlierSampleCount",
      "value": "2",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-012"
    }
  ],
  "criticalFindings": [],
  "warnings": [],
  "audit": {
    "inputHash": "sha256:<calculated at runtime>",
    "rulesVersion": "<utility executable version>",
    "documentation": "GenomeWideSignalBatchChecker.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