Metabolomics & Mass Spectrometry Data Analysis

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Overview

Metabolomics data is rich and unusually easy to over-interpret. We process untargeted and targeted LC-MS and GC-MS data from raw files through peak detection, alignment, normalisation and statistical analysis to pathway interpretation — and we report metabolite identifications with confidence levels rather than presenting every annotation as a confirmed compound. Quality control samples, drift correction and batch structure are treated as central, because analytical variation is often larger than the biology.

Schematic: raw reads become a quantified matrix, then tested differences, then annotated populations — with the statistics matched to the study design.

Key Advantages

Identification Confidence Levels Stated

Annotations are reported against community confidence levels, so a match on accurate mass alone is clearly distinguished from one confirmed with a standard.

Drift And Batch Corrected

Pooled quality control samples are used to correct signal drift and batch effects, and the correction is shown rather than silently applied.

Statistics Appropriate To The Data

Univariate and multivariate methods are used together, with cross-validation and permutation testing that guard against the overfitting endemic to metabolomics studies.

Pathway Interpretation With Context

Enrichment is performed against the measurable metabolome rather than the entire database, which substantially changes which pathways come out as significant.

Where This Service Is Used

Comparing metabolic profiles between disease and control groups

Characterising metabolic response to a drug, diet or environmental exposure

Discovering candidate metabolite biomarkers for later targeted validation

Integrating metabolomics with transcriptomics or proteomics for pathway-level interpretation

Profiling natural product extracts and secondary metabolites

Methods and Techniques Applied

Raw data conversion, peak picking, retention time alignment, grouping and gap filling

Quality control assessment with pooled samples, drift correction and batch adjustment

Normalisation, transformation and scaling chosen for the data and documented

Univariate testing with multiple testing correction and multivariate modelling with proper validation

Metabolite annotation against spectral libraries with confidence level assignment, including molecular networking where useful

Pathway and enrichment analysis against an appropriate measurable background

What We Need From You

Raw mass spectrometry files with the chromatographic and acquisition method described

Sample metadata including group, batch, run order and the quality control sample scheme

Any internal standards used and, for targeted work, the compound list and calibration data

The biological question and which comparisons matter

What You Receive

Processed feature table with retention time, mass, intensity and quality metrics

Annotated metabolite list with identification confidence levels

Statistical results including univariate tables and validated multivariate models

Pathway analysis and integrated interpretation with other omics layers where supplied

Full report with methods, parameters, library versions and analysis code

What We Compute and Report

Feature tables with retention time, accurate mass, intensity and quality metrics

Metabolite annotations reported against community identification confidence levels

Drift and batch correction results shown before and after adjustment

Univariate statistics and cross-validated multivariate models with permutation testing

Pathway enrichment computed against the measurable metabolome as background

Interpretation and Limitations

Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.

Specifications

Item	Specification	Notes
Platforms	LC-MS, GC-MS, targeted MRM	Untargeted and targeted workflows
Samples per study	20–1,000+	With pooled quality control samples interspersed
Typical turnaround	3–6 weeks	Annotation and curation dominate the timeline
Output	Tables, models, figures, code	With confidence levels per annotation

Service Workflow

Step 01

Scientific Consultation

We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.

Step 02

Feasibility Review

We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.

Step 03

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.

Step 04

Computation and QC

The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.

Step 05

Report and Follow-Up

You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.

Is This the Right Service for You?

Choose this service when

Untargeted profiling needs processing, annotation and statistics that will survive review

Analytical drift and batch structure risk swamping the biological signal

Metabolite candidates need prioritising for targeted validation

Consider another service when

The question is about proteins rather than small molecules

Proteomics & PTM Analysis

Structural characterisation of a purified compound is required

Polysaccharide Structure & Spectral Analysis

What Goes Wrong, and How We Handle It

The failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Annotations are reported against community confidence levels, so a mass-only match is visibly different from a standard-confirmed one.

Case Studies

The situation

An untargeted study produced a strongly discriminating feature that the team wanted to develop into an assay.

What the work involved

Pooled quality control samples were used to characterise drift, batch and run-order effects were modelled, and the analysis was repeated after correction.

What came back

The feature tracked run order almost perfectly and did not survive correction. Four other features did survive, and were taken forward to targeted quantification instead.

Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.

Questions We Are Asked Before Scoping

Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.

Get in Touch
about Metabolomics & Mass Spectrometry Data Analysis

Tell us about the target, the data you hold and the decision the result needs to support. A scientist reviews every enquiry and replies within one business day.

Feasibility reviewed before any scope or price is fixed

Methods, parameters, confidence and limitations in every report

Confidentiality agreement available before data is shared