Adduct, Isotope Grouping & Feature Linking — Hands-on

Learn how to group adducts, isotopes and in source fragments into coherent features that reflect underlying metabolites. You will work with adduct chemistry, isotopic patterns, correlation based grouping and network style feature linking to clean up LC MS metabolomics peak tables for annotation and quantitation.

Adduct, Isotope Grouping & Feature Linking

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Session Index

Session 1 — Adduct Chemistry & Ion Species Session 2 — Isotope Patterns & Grouping Rules Session 3 — Feature Grouping & Neutral Mass Inference Session 4 — Network Style Feature Linking & QA

Session 1

Fee: Rs 8800

Adduct Chemistry & Ion Species

Common adducts in positive and negative mode

[M+H]+ [M+Na]+ [M+K]+ [M+NH4]+ and in source dimers [M-H]- [M+formate]- [M+acetate]-

Impact of mobile phase and matrix on adducts

buffer composition and salts contaminants and plasticizers instrument history effects

In source fragments and neutral loss patterns

typical neutral losses relationship to parent ions avoiding double counting in tables

Session 2

Fee: Rs 11800

Isotope Patterns & Grouping Rules

Natural isotope patterns for metabolites

13C 15N 34S contributions M M+1 M+2 envelopes resolution and pattern visibility

Isotope grouping criteria in peak tables

m z spacing constraints RT and shape similarity intensity ratio expectations

Distinguishing isotopes from separate features

overlap and coelution scenarios use of correlation across samples manual review strategies

Session 3

Fee: Rs 14800

Feature Grouping & Neutral Mass Inference

Adduct and isotope grouping in common tools

MZmine and MS DIAL style grouping adduct libraries and rules feature group identifiers

Inferring neutral mass from grouped features

back calculating neutral mass consistency checks across adducts handling conflicting assignments

Collapsing groups to representative features

choosing quantitative ion keeping links to all members export formats for downstream tools

Session 4

Fee: Rs 18800

Network Style Feature Linking & QA

Correlation and network based linking

sample wise intensity correlation

Visualizing feature relationships as networks

edge types for adduct and isotope links cliques and connected components identifying suspicious clusters

QA checks and export of grouped feature tables

group size distributions flagging orphan and noisy features final tables for annotation pipelines

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Adduct, Isotope Grouping & Feature Linking Training | LC MS Feature Networks for Metabolomics

Adduct, Isotope Grouping & Feature Linking — Hands-on