Peak Detection, Alignment & Deconvolution — Hands-on

Learn to convert LC MS chromatographic data into high quality feature tables for metabolomics and lipidomics. You will configure peak picking, perform retention time alignment, handle deconvolution in complex regions and curate peak tables ready for statistical analysis and biological interpretation.

Peak Detection, Alignment & Deconvolution

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Session Index

Session 1 — Chromatographic Peaks & Noise Session 2 — Peak Picking Algorithms & Parameters Session 3 — Retention Time Alignment Strategies Session 4 — Deconvolution, Gap Filling & Peak Table QA

Session 1

Fee: Rs 8800

Chromatographic Peaks & Noise

TIC, BPC and extracted ion chromatograms

TIC overview base peak views XIC for individual features

Peak shapes, baselines and noise sources

Gaussian like peaks tailing and fronting chemical and electronic noise

Signal to noise and detection limits intuition

estimating noise regions S N based thresholds implications for low abundance features

Session 2

Fee: Rs 11800

Peak Picking Algorithms & Parameters

Feature detection workflows in common tools

generic concepts behind XCMS like tools MZmine style workflows MS DIAL style workflows

Key parameters for robust peak detection

m z tolerance and RT tolerance minimum peak height minimum peak width and scans

Evaluating results after first peak picking pass

feature counts per sample blank versus sample comparison review of representative chromatograms

Session 3

Fee: Rs 14800

Retention Time Alignment Strategies

Sources of RT drift across runs and batches

temperature and column aging gradient and pump effects sample queue influences

Alignment models and reference choices

landmark based alignment non linear warping concepts use of pooled QC as reference

Diagnostics after RT alignment

before after RT plots feature level RT spread impact on missingness and grouping

Session 4

Fee: Rs 18800

Deconvolution, Gap Filling & Peak Table QA

Deconvolution in crowded chromatographic regions

overlapping peaks and shoulders

Gap filling and handling missing intensities

re extraction around expected RT and m z minimum intensity rules difference between zero and missing

Peak table curation and export for statistics

filtering by blanks and RSD basic feature level QC flags export to matrix and metadata files

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Peak Detection, Alignment & Deconvolution Training | LC MS Feature Extraction for Metabolomics

Peak Detection, Alignment & Deconvolution — Hands-on