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IP4M edited this page Aug 23, 2020 · 57 revisions


IP4M: An integrated platform for mass spectrometry-based metabolomics data analysis

IP4M is an integrated platform for mass spectrometry-based metabolomics data analysis. It contains 62 independent tools that cover almost all the basic and advanced demands of computational metabolomics, including raw data visualization and peak picking, peak identification, peak table preprocessing, difference analysis, correlation analysis, cluster and sub-cluster analysis, linear regression analysis, pathway and enrichment analysis, ROC analysis, Venn analysis, sample size and power analysis, and many others (Figure 1).

The strengths of IP4M are comprehensive functions and useful tools, rich knowledgebase, and options for customizable operations and integrated workflows. In the near future, some of the modules will be moved to an online platform. Compared with existing non-commercial tools, IP4M makes advances in 3 aspects. 1) Some ratio variables (intensity of product / intensity of substrate) will be generated and be involved in differential and correlation analysis. These ratios, which may partially reflect the activity of metabolic enzymes and reactions, provide broader information than that of original metabolomics data. 2) A new method (GRaMM), which was designed and developed recently for the inter-correlation detection between metabolome and microbiome data has been embedded. 3) The pathway analysis module with rich knowledgebase and extended algorithms is beneficial to data interpretation. Some demo outputs are shown as Figure 2.

Figure 1

Figure 2

Availability and requirements

Source code and manual:
Operating system(s) : Windows 2007 or 2010; Ubuntu 16.04 or 18.04; macOS Catalina 10.15
Programming language : Java, Perl, R, Eclipse RCP
License : GNU GPL.V3
Restrictions to non-academic use : licence needed


For Windows users:

  1. Download "" and unzip the archive.
  2. Launch the programme with "IP4M.exe".
    Note: All the necessary third-party binaries and libraries have been integrated already. No installation is required. Administrator privilege is required. The programme has been tested in Windows 7 and Windows 10.

For Linux users:

  1. Download "IP4M_Ubuntu_Config.tar.gz" and decompress it.
  2. Download "IP4M-2.0-Linux64.tar.gz" and decompress the archive.
  3. Run "sh" in the IP4M directory.
  4. Launch the programme with "IP4M".
    Note: The programme has been tested in Ubuntu 16.04 and Ubuntu 18.04.

For MacOS users:

  1. Download "IP4M_Mac_Config.tar.gz" and decompress it.
  2. Download "IP4M-2.0-macOS64.tar.gz" and decompress the archive.
  3. Run "sh" in the IP4M directory.
  4. Launch the programme with "IP4M".
    Note: The programme has been tested in macOS Catalina version 10.15.


1 .
2 . IP4M-2.0-Linux64.tar.gz
3 . IP4M-2.0-macOS64.tar.gz
4 . IP4M_Ubuntu_Config.tar.gz
5 . IP4M_Mac_Config.tar.gz
6 . IP4M-2.0-Demo
7 . Manual 2.0.pdf
8 . How to use.mp4 (video)

Brief Manual

See a detailed manual (.pdf) here .

1.Interface structure and workflow

The software interface includes four parts: tools window, main window, task window and file window. Workflow (Fig. 3): select a tool in tools window–> Set parameters and execute in main window –> View running status in task window –> View results in main and file window. Fig. 3 Workflow of usage

Specific steps:

  1. In the tools window, double-click the tool you want to use and the parameters setting panel will pop up automatically (Fig. 4).

Fig. 4 Select a tool

  1. Use the default parameters or edit them as you want. Click the “Execute” button to run and the corresponding task information will appear in the task window (Fig. 5). Fig. 5 Execute the task

  2. When the task is finished, double-click the task to view the list of result files in main window and file window (Fig. 6). Click the files to view the specific results (Fig. 7). Fig. 6 View the results 1 Fig. 7 View the results 2

  3. If the task has failed, you can double-click the task item to view the log information (Fig. 8). Fig. 8 View the log information

  4. Right-click on the task item and select ‘Rerun’ to edit the inputs and/or parameters as the error messages and then re-run the task (Fig. 9). Fig. 9 Rerun the task

2.Input files and formats

Raw data of mzXML, mzML and NetCDF formats and other files (peak table, sample information, compound list) of tab-delimited text format are supported. The free software msconvert of ProteoWizard ( is recommended for converting raw data files from various instrument vendors into mzXML, mzML format. Table 1 The input files and formats for the modules.

Module Tool Input file (example file)
Raw data preprocessing LC-MS preprocessing cdf / mzXML / mzML
GC-MS preprocessing
Peak annotation LC-MS peak annotation Peak table (file1)
GC-MS peak annotation Peak table (file1); msp (file3)
Peak table operations Variable expansion Peak table with compound name (file2)
Outlier processing Peak table (file2)
Zero filling
Normalization Peak table (file2);
Sample-to-QC design file (file4)
Basic statistics summary Peak table (file2)
Retrieve rows from peak table Peak table (file2);
One-column metabolite list file (file5)
Row averages by gorups Peak table (file2);
Sample-to-group design file (file6)
Transformation Peak table (file2)
Merge tables
Statistical analysis Student T-test Peak table (file2);
Sample-to-group design file (file6)
Analysis of variance
Kruskal-Wallis rank sum test
PCA Peak table (file2)
PLSDA Peak table (file2);
Sample-to-group design file (file6)
Pathway and enrichment analysis Compounds ID mapping One-column metabolite list file (file5)
Pathway analysis Compound ID mapping results (file7)
Enrichment analysis
workflow GC-MS data preprocessing workflow cdf / mzXML / mzML
LC-MS data preprocessing
Statistical analysis Peak table (file2);
Sample-to-group design file (file6)
Pathway and enrichment analysis One-column metabolite list file (file5)
Other tools GLM on two groups Peak table (file2);
Sample-to-group design file (file6)
ROC analysis
Hierarchical cluster analysis Peak table (file2)
Plot heatmap
Subcluster analysis
Correlation ana distance analysis Intra features correlation Peak table (file2)
Inter features correlation
Inter features partial correlation
Intercorrelation between metabolomics and microbiome Peak table (file2);
Microbiome table (file8)
Data of covariates (file9)
Generate distance matrix Peak table (file2)
Make distance heatmap Distance matrix (file10)
Retrieve pairs for Cytoscape Distance or correlation matrix (file10)
Plotting tools Plot venn diagram Identifiers list file (file11)
Pairwise scatter plot Peak table (file2)
Box plot
Line or bar chart
Violin plot Peak table (file2)
Sample-to-group design file (file6)

Example of input files

1) Peak table (file1)

2) Peak table (file2)

3) msp file (file3)

4) Sample-to-QC design file (file4)

5) One-column metabolite list file (file5)

6) Sample-to-group design file (file6)

7) Compound ID mapping results (file7)

8) Microbiome table (file8)

9) Data of covariates (file9)

10)Distance matrix (file10)

11) Identifiers list file (file11)

3.Demos of the outputs of main modules

All the intermediate and final results are exported as .txt (data and tables) or .pdf (figures) files.

Figure. 10. Outputs of LC-MS preprocessing (peak identification)

Figure. 11. Outputs of GC-MS peak annotation with the identified peak table and detailed information files.

Figure. 12. Basic statistics computation.

Figure. 13. Results of t-test.

Figure. 14. The results by OPLSDA

Figure. 15. The results by SVM.

Figure. 16. The results by Boruta.

Figure. 17. The results by Biosgner.

Figure. 18. Compound ID mapping.

Figure. 19. Pathway bubble plot and pathway bar plot.

Figure. 20. Outputs of LC-MS preprocessing workflow.

Figure. 21. outputs of statistical analysis workflow.

Figure. 22. outputs of other tools.

Bug report (contact);;