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MTEApy

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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.

Installation

To install MTEApy, you can install it using pip:

pip install mteapy

Or you can download this repository and install it locally, again using pip:

git clone https://github.com/bsc-life/mteapy/
pip install -e mteapy/

Overview

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 which indicates the metabolic activity of the metabolic tasks across samples.
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 which indicates the change in metabolic activity for one control-sample. 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 which indicates the change in metabolic activity for one control-sample. 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.

Command-line

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.

Python module

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

Additionaly, you can import some helper functions from the mteapy.utils module, including the mask_lfc_values function, which can be used to mask non-significant log-FC values to 0 for the TIDE frameworks (this feature can be selected directly from the command-line by using the flag --maks_lfc_values).

from mteapy.utils import mask_lfc_values

Documentation

Visit bsc-life.github.io/mteapy to check the package documentation and tutorials.

Citation

Comming soon!

Contact

References

  1. 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.
  2. 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.
  3. 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.