How can I analyse my metabolomics data in Mass Dynamics?

This workflow is designed for metabolomics datasets where each metabolite or feature has a quantitative intensity value for each sample. It is most commonly used for discovery metabolomics and relative quantification workflows.

When to use this workflow

Use this workflow when you have metabolomics intensity data to upload to Mass Dynamics for analysis, visualisation, comparison, or downstream interpretation.

To upload data from these tools, first prepare the data according to the MD Format — Metabolite.

What the data should contain

Your dataset should contain one quantitative value per metabolite, or feature, per sample.

Mass Dynamics expects metabolomics values to be uploaded as non-logged intensity values.

You may upload raw, normalised, or otherwise analysis-ready intensity values, depending on your workflow. However, do not upload log-transformed intensity values. Normalisation and imputation can also be performed in Mass Dynamics after upload.

Required files

A metabolomics upload prepared as MD Format — Metabolite requires three files:

File	Purpose
Metabolite intensity file	Contains metabolite or feature intensity values for each sample
`experiment_design.csv`	Maps samples to the experimental design
`sample_metadata.csv`	Contains sample-level annotations such as condition, batch, timepoint, or treatment

The sample names must match exactly across all files.

Identifier guidance

The most important requirement is that each metabolite or feature has a stable, consistent identifier.

You can use identifiers such as:

Identifier type	Example use
InChIKey	Structure-based metabolite identifier
ChEBI	Stable metabolite identifier
HMDB	Metabolomics database identifier
KEGG Compound	Pathway and compound database identifier
PubChem CID	Cross-reference identifier
Internal feature ID	Stable identifier from your own workflow or processing pipeline

You can choose the identifier that best fits your dataset. The key requirement is consistency.

The identifier you provide is used to link and compare entities. If you plan to compare or map metabolites across datasets in Mass Dynamics, use the same identifier system across those datasets wherever possible.

Unknown or partially annotated features

Discovery metabolomics datasets often contain features that are not fully identified.

These can still be uploaded, provided each feature has a stable and unique identifier. For example, you may use an internal feature ID from your processing pipeline.

Additional feature information can be included as metadata columns, such as:

Metadata column	Example
`MetaboliteName`	Glucose
`AnnotationStatus`	Putative
`mz`	345.123
`RetentionTime`	5.67
`Formula`	C6H12O6
`HMDB`	HMDB0000122
`KEGG`	C00031
`Pathway`	Glycolysis

Pathway enrichment

Pathway enrichment requires suitable metabolite identifiers, such as HMDB, KEGG Compound, ChEBI, or another supported identifier.

Mass Dynamics can only link entities based on the identifiers and metadata provided in your upload. For this reason, we recommend using stable identifiers consistently across datasets.

Recommended upload preparation

Before uploading, check that:

Requirement	Why it matters
Your intensity values are not log-transformed	Mass Dynamics expects non-logged quantitative values
Each metabolite or feature has a stable ID	Enables consistent mapping and comparison
Every sample has a value for every metabolite or feature	Ensures a complete matrix for downstream analysis
Missing values are represented correctly	Prevents missingness from being treated as real measured zeroes
Sample names match across all files	Allows Mass Dynamics to connect intensity values to metadata and experimental design

Next step

Once your metabolomics data has been prepared, upload it using MD Format — Metabolite.