A web app implementation of the statistics notebooks for metabolomics by the Functional Metabolomics Lab. These notebooks are developed by the Virtual Multi Omics Lab (VMOL).

Installation

• run the app without installation (recommended for smaller datasets)

Local Installation

You can run the Statistics for Metabolomics app in two ways:

1. From source code

   • Download or clone this repository.
   • Open a terminal in the project folder and run:

     pip install -r requirements.txt
     streamlit run Statistics_for_Metabolomics.py

   • The app will open automatically in your browser.

2. Using the Windows executable (.msi)

   • Download the latest .msi version from the releases page.
   • ⚠️ Before installing a new version, delete all files from any older installation (for example, from C:/) or (Control Panel >> Programs >> Programs & Features >> Uninstall a Program >> remove FBMN-STATS-GUIed)
   • This helps avoid compatibility or version conflicts so the app runs smoothly.
   • Once downloading, if the pop-up terminal seems blank or isn't loading, restart the app.

Available Statistics

• Principal Component Analysis (PCA)
• Multivariate
  • PERMANOVA & PCoA
• Hierachical Clustering & Heatmaps
• Univariate
  • One-way ANOVA & Tukey's post hoc test
  • Kruskal-Wallis & Dunn's post hoc test
• Student's t-test

Quickstart

Once you have completed the Data Preparation step, chose any of the available statistics sections.

Data Preparation

• two tables are required: Quantification and Meta Data
• supported formats: tsv and txt (tab separated), csv (comma separated) and xlsx (Excel file)
• if feature table has an optional metabolite column that will be taken as index (can be unique ID, contain m/z and RT information or actual metabolite name)
• feature index can be automatically generated if columns for m/z and RT (and optionally row ID) are present
• sample file names need to contain mzML file name extensions
• quantification table needs sample file names as column names
• meta data table requires a filename column
• meta data table can contain columns with attributes
• checkout the example data availabe in file selection
• remove blank features and impute missing values in the Data Cleanup section

Example feature table:

metabolite	sample1.mzML	sample2.mzML	blank.mzML
1	1000	1100	100
2	2000	2200	200

Example meta data table:

filename	Sample_Type	Time_Point
sample1.mzML	Sample	1h
sample2.mzML	Sample	2h
blank.mzML	Blank	N/A