Normalization, Batch Correction & Data Integrity — Hands-on

Learn how to turn raw metabolomics feature tables into analysis-ready matrices with robust normalization, batch correction and traceable data integrity. You will implement QC-based scaling, drift correction, inter-batch harmonization, and build audit-ready logs for reproducible downstream statistics and regulatory-facing work.

Normalization, Batch Correction & Data Integrity

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Session Index

Session 1 — Intensity Distributions & Normalization Principles Session 2 — QC-Based Normalization & Drift Correction Session 3 — Batch Effects, Merging Batches & Study Design Session 4 — Data Integrity, Audit Trails & Freeze-Down Matrices

Session 1

Fee: Rs 8800

Intensity Distributions & Normalization Principles

Visualising raw feature intensities and spread

density plots and boxplots sample-wise total intensity feature-wise variance checks

The role of normalization in metabolomics

technical versus biological variation compositional effects and scaling impact on downstream statistics

Common normalization strategies and assumptions

total area and median normalization probabilistic quotient concepts log transform and scaling choices

Session 2

Fee: Rs 11800

QC-Based Normalization & Drift Correction

Using pooled QC samples for monitoring drift

QC injection frequency QC intensity versus run order plots RSD thresholds for features

QC-based signal correction strategies

LOESS / spline style drift models feature-wise versus global approaches pre and post correction diagnostics

Internal standards and normalization anchors

spiked internal standards per class monitoring internal standard stability hybrid QC + internal standard schemes

Session 3

Fee: Rs 14800

Batch Effects, Merging Batches & Study Design

Detecting batch and plate effects

PCA coloured by batch boxplots by plate/run day feature-wise batch statistics

Batch correction methods and cautions

location/scale adjustments empirical Bayes style approaches risks of removing biological signal

Designing multi-batch studies up front

blocking and randomization bridge samples and overlap design planning for future batch merges

Session 4

Fee: Rs 18800

Data Integrity, Audit Trails & Freeze-Down Matrices

Tracking transformations from raw to final matrix

stepwise processing manifests scripted versus manual edits versioning of feature tables

Data integrity checks and anomaly flags

range and missingness rules duplicate consistency checks pre-analysis sanity reports

Freeze-down matrices for long term projects

final normalized & batch-corrected tables linked metadata and code snapshots hand-off packages for collaborators and regulators

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Normalization, Batch Correction & Data Integrity Training | QC-Driven Metabolomics Preprocessing

Normalization, Batch Correction & Data Integrity — Hands-on