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Bioinformatics Platform
iMetAct v2.0 — Inference of Metabolic Enzyme ActivityData sourced from TCGA Research Network. For research use only.
陕ICP备2023012414号-1
A Bioinformatics Platform for Cancer Metabolism Research

Infer Metabolic Enzyme Activity from Gene Expression

iMetAct enables you to explore metabolic subtypes across 33 TCGA cancer types, analyze enzyme-survival associations, predict HCC metabolic subtypes, and compute sample-level enzyme activity — all powered by iMetAct and NMF algorithms.

Explore Cancer SubtypesAnalyze Your Data
33Cancers
2,678Enzymes
10k+Samples
4Modules

Analysis Modules

Explore cancer metabolic subtypes, analyze enzyme-survival associations, predict HCC subtypes, and infer enzyme activity for your own samples

Cancer Stratification

Explore metabolic subtypes across 33 TCGA cancer types using NMF clustering with KEGG and Reactome pathway databases.

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Metabolic Analysis

Analyze individual metabolic enzymes and their association with patient survival outcomes.

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Subtype Prediction

Upload HCC RNA-seq data to predict metabolic subtypes using a random forest classifier trained on TCGA data.

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Samples Activity

Upload your expression matrix to infer metabolic enzyme activity for your samples against TCGA reference.

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Data Download

Download pre-computed metabolic enzyme activity matrices, supplementary tables, and clinical data.

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How iMetAct Works

A streamlined workflow from data selection to result download

1

Select Cancer Type

Choose from 33 TCGA cancer types with pre-computed metabolic enzyme activity

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2

Configure Analysis

Set parameters for clustering, survival analysis, or enzyme activity inference

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3

Run & Review

Execute the analysis and explore interactive visualizations and results

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4

Download Results

Export figures, tables, and full activity matrices for your research

Data Coverage

Comprehensive metabolic enzyme activity across the TCGA pan-cancer atlas

3333 Cancer TypesComprehensive TCGA coverage
2,6782,678 EnzymesReactome metabolic genes
44 Analysis ModulesStratification, analysis, prediction, inference
10k+10,000+ SamplesTCGA patient samples processed

Citation

If you use iMetAct in your research, please cite:

Wang, B., Huang, C., Liu, X., Liu, Z., Zhang, Y., Zhao, W., Xu, Q., Ho, P. C., & Xiao, Z. (2025). iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment. Cell Reports, 44(3), 115375.

https://doi.org/10.1016/j.celrep.2025.115375
View PublicationPlease also cite the TCGA Research Network: https://www.cancer.gov/tcga