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MTEApy is a Python library and command-line tool for Metabolic Task Enrichment Analysis (MTEA) that leverages powerful constraint-based metabolic model frameworks. It uses metabolic tasks to infer the metabolic phenotype or changes in metabolic pathway expression based on transcriptomic data.
To install MTEApy, you can install it using pip:
pip install mteapyOr you can download this repository and install it locally, again using pip:
git clone https://github.com/bsc-life/mteapy/
pip install -e mteapy/MTEApy is comprised of two main constraint-based metabolic modelling frameworks, TIDE and CellFie, implemented in Python (the original source codes are published in Matlab at their respective repositories). Each framework runs using different types of input files.
| Framework | Original Code | Description |
|---|---|---|
| CellFie [1] | LewisLabUCSD/CellFie | Utilises a normalized expression matrix (e.g., TPMs) to compute a gene activity score using user-defined thresholds, and then projects it into metabolic reactions. Using the participating reactions for each metabolic task, a metabolic score is computed. |
| TIDE [2] | csbl/iCardio | Utilises a differential expression result and its log-FC values to project them into metabolic reactions. Using the participating reactions for each metabolic task, a metabolic score is computed. A p-value is assigned to each score after performing a permutation test. |
| TIDE-essential | bsc-life/mteapy | Utilises a differential expression result, its log-FC and essential genes to metabolic tasks to compute a metabolic score. A p-value is assigned to each score after performing a permutation test. |
MTEApy is designed to be used both as a command-line tool and as a Python module in a Jupyter Notebook or Python script.
If used as a command-line tool, run the command run-mtea and specify the desired framework. By default, the metabolic model used by the command is the Human-GEM [3] and, therefore, the metabolic tasks are also compatible with Human-GEM.
run-mtea [-h] [-v] [-c] [-t] [-s] {TIDE-essential,TIDE,CellFie}For more details on the input parameters, run the -h or --help after any of the commands.
If used as a Python module, import the mteapy module or directly import the desired wrapper functions to compute a framework.
from mteapy.tide import compute_TIDE, compute_TIDEe
from mteapy.cellfie import compute_CellFie[TO DO]
- Richelle, A.; Kellman, B.P.; Wenzel, A.T.; Chiang, A.W.; Reagan, T.; Gutierrez, J.M.; Joshi, C.; Li, S.; Liu, J.K.; Masson, H.; et al. Model-based assessment of mammalian cell metabolic functionalities using omics data. Cell Reports Methods 2021, 1, 100040. https://doi.org/10.1016/j.crmeth.2021.100040.
- Dougherty, B.V.; Rawls, K.D.; Kolling, G.L.; Vinnakota, K.C.; Wallqvist, A.; Papin, J.A. Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. Cell Reports 2021, 34, 108836. https://doi.org/10.1016/j.celrep.2021.108836.
- Robinson, J.L.; Kocabaş, P.; Wang, H.; Cholley, P.E.; Cook, D.; Nilsson, A.; Anton, M.; Ferreira, R.; Domenzain, I.; Billa, V.; et al. An atlas of human metabolism. Science Signaling 2020, 13, eaaz1482. https://doi.org/10.1126/scisignal.aaz1482.
- Xavier Benedicto Molina ([email protected])
- Miguel Ponce-de-León ([email protected])