GEMCAT: Gene Expression-based Metabolite Centrality Analyses Tool

A computational toolbox associated with the manuscript entitled "GEMCAT - A new algorithm for gene expression-based prediction of metabolic alterations". Cite using: https://www.biorxiv.org/content/10.1101/2024.01.15.575710v1

Compatibility

The package is tested for compatibility with Python >= 3.10 on Ubuntu and Windows.

Installation

Install from pip:

pip install gemcat

Or clone the repository and install GEMCAT from there using:

pip install .

Usage

Standard workflow from the Command-Line Interface (CLI)

Use a single file containing per-gene fold-changes to calculate the resulting differential centralities: gemcat ./expression_file.csv ./model_file.xml -e column_name -o <result_file.csv> Make sure the .csv file is either comma- or tab-delimited. column_name is the name of the column in the file containing the fold-change.

Alternatively, use two files (or one file) with expression values for condition and baseline: gemcat <./condition_file.csv> <./model_file.xml> -e <condition_column_name> -b <./baseline_file> -c <baseline_column_name> -o <result_file.csv>

Currently only models in XML/SBML format are supported in the CLI. Further models can be used from the Python library. Support will come to the CLI soon.

Important points to remember: Your gene or protein identifiers should be the first column of the expression file. Make sure the gene or protein identifiers in your expression data file exactly match those in the model. A results list of all 1.0 is a sure sign of no identifier matching.

positional arguments:

• expression file path
• model file path

All parameters: -e --expressioncolumn name of column containing condition expression data -b BASELINE, --baseline file containing baseline expression data -c BASELINECOLUMN, --baselinecolumn name of column containing baseline expression data -v VERBOSE, --verbose enables verbose output -o OUTFILE, --outfile write output to this file -l LOGFILE, --logfile write logs to this file

Standard workflow in Python using a CobraPy model

import gemcat as gc
results = gc.workflows.workflow_standard(
  cobra_model: cobra.Model,
  mapped_genes_baseline: pd.Series,
  mapped_genes_comparison: pd.Series,
  adjacency = gc.adjacency_transformation.ATPureAdjacency,
  ranking = gc.ranking.PagerankNX,
  gene_fill = 1.0
)

This will return the changes in centrality relative to the baseline in a Pandas Series. When using fold-changes as the mapped expression, use a vector of all ones as a comparison.

Modularity and Configuration

GEMCAT is designed to be modular, and its central components can easily be swapped out or appended by other components adhering to the specifications laid out in the module base classes (primarily adjacency transformation, expression integration, and ranking components). All classes inheriting from the abstract base classes laid out in the modules are swappable.

Core modules

Model

The core of the package is the GEMCAT model structure that contains the model data, integrates the workflow, and calculates the results.

Adjacency

Different approaches can be used to calculate adjacency in the networks. We offer alternatives and a platform to create custom algorithms for the model.

Expression

Module covering the mapping of gene values onto reactions in the model via gene product rules. Providing different algorithms along with a platform to create alternatives.

Pagerank

Module providing ranking algorithms for the models along with a platform to include custom algorithms.

workflows

The workflow module contains example workflows. To customize the workflow to your needs simply copy the provided functions and switch out the desired steps.

io

Input and output functions that create GEMCAT models from different sources.

utils

Contains common utility functions used throughout the package.

verification

Functions to verify data integrity.

Development

You can run all local tests with pytest .. Default behavior is to also run integration tests, which takes time. You can exclude slow running tests by using pytest . -m "not slow". These slow running tests are integration tests with "real world data" and will take 10-30s each according to your hardware.

To run tests, make sure you have git lfs installed and all the Tests are running. Make sure to run isort and black to have properly formatted code.

The CI pipeline in Github will check with isort, black, and pytest.