This archive contains an assortment of scripts used for analysing dynamic gadoxetate-enhanced magnetic resonance imaging (MRI) signal data in rat liver as part of the work conducted by work package 2 (WP2) of the IMI-TRISTAN TRISTAN EU project.
These scripts accompany the following studies:
Six Test Compoundsstudy (1)Reproducibilitystudy (manuscript in preparation)
A working Python environment is required to run the scripts contained within
this repository. All required Python dependencies are specified within the
requirements.txt file located within the root directory of this project.
In this project, conda virtual environments were used to manage the project
dependencies in isolation. Anaconda may be installed within the user's directory
without causing conflicts with a system's Python installation, therefore it is
recommended to set up a working environment by downloading the conda package
manager from Anaconda's Python distribution.
PLEASE NOTE: The following steps assume that Anaconda has already been installed and that commands are run from a Windows OS. If replicating from a different OS, please adapt commands to the appropriate related invocation (Some examples here).
-
Open Command Prompt in an interface of your choice, create an empty project directory and navigate into it by inputting the following commands:
mkdir TRISTAN-rat cd TRISTAN-rat -
Clone the TRISTAN-rat repository from GitHub into the newly created directory:
git clone https://github.com/QIB-Sheffield/TRISTAN-rat .Alternatively, download the whole archive locally as a zip file. More details on cloning a repository may be found here.
-
From the project root directory, run the following command to create a separate virtual environment:
conda create --name <environment_name> python=3.7 -
Activate the virtual environment:
conda activate <environment_name> -
Install required Python dependencies:
pip install -r requirements.txt
All necessary source code for reproducing the results from each
study are located within the src directory of this project. In
its current form, the majority of scripts in this directory are
intended to be used specifically for reproducing these results
along with the data accompanying these studies. If the user wishes
to analyse their own DCE-MRI data with the TRISTAN-rat model and
define their own outputs, please see section Interacting with the scripts for guidelines on how to do this.
Please download the data.zip and results.zip folders from
https://zenodo.org/record/7506968#.Y7cNCdXP1PY
(DOI: 10.5281/zenodo.7506968) and extract the contents. Please
make sure the extracted contents are stored within the project's
root directory.
The results for this study are generated using the
SixTestCompounds.py script located in the src directory.
From within the project's root directory, run the script using
the following command:
python src/SixTestCompounds.py --study SixTestCompounds
This will create a results folder with the following structure:
results/
|
|---- StudyName (e.g., 'SixTestCompounds')/
| |
| |---- DateOfAnalysis YYYY-MM-DD (e.g., '2022-11-04')/
| | |
| | |---- 01_model_outputs/
| | | |---- figures/
| | | | |---- per_substudy/
| | | | |---- per_rat/
| | | |---- relaxation_rates_and_signals
| | | |---- all_parameters.csv
| | | |---- fit_errors.txt
| | |---- 02_analyses/
| | | |---- figures/
| | | |---- repeatability/
As the tracer kinetic model used in this study produces estimated parameter
variables, modelling outputs may vary slightly between different iterations.
Therefore, upon each execution of the code, a top-level directory named after
the date the analysis was conducted is created for storing the results from
that particular execution. For reference, the results and figures presented in
the accompanying manuscript to the Six Test Compounds study were created using the
outputs contained in results/SixTestCompounds/2022-09-01. Please note that the
results contained in the Zenodo archive were created using a previous release of this
software (https://github.com/QIB-Sheffield/TRISTAN-rat/releases/tag/v1.0.0) which
ouput a slightly modified results folder structure. Please see the previous version
notes for more details.
01_model_ouputs contains all outputs generated as result of the tracer kinetic
model fitting. Within this, plotted signal time curves for each acquistion per
rat may be found in figures/per_rat, while average deltaR1 curves per drug may
be found in figures/per_drug. All estimated parameter variables from the fitting
are stored in all_parameters.csv. The folder relaxation_rates_and_signals
contains the fitted MRI signal data in a similar format to the original csv data
in data/01_signals. Additional columns have been included showing the MRI signals
converted to R1.
02_effect_sizes contains results of the statistical analysis performed on the
tracer kinetic modelling output. This is summarised in tabular format in
effect_sizes.csv, while graphical distributions are provided within the figures/
folder. fit_errors.txt contains an ID list for any computational fitting errors
found during quality control of the tracer kinetic modelling.
Please download the data.zip and results.zip folders from
https://zenodo.org/record/7838397#.ZD3jlvzMJPY
(DOI: 10.5281/zenodo.7838397) and extract the contents. Please
make sure the extracted contents are stored within the project's
root directory.
The results for this study are generated using the
Reproducibility.py script located in the src directory.
From within the project's root directory, run the script using
the following command:
python src/Reproducibility.py --study Reproducibility
This will create a results folder with the following structure:
results/
|
|---- StudyName (e.g., 'Reproducibility')/
| |
| |---- DateOfAnalysis YYYY-MM-DD (e.g., '2023-04-14')/
| | |
| | |---- 01_model_outputs/
| | | |---- figures/
| | | | |---- per_substudy/
| | | | |---- per_rat/
| | | |---- relaxation_rates_and_signals
| | | |---- all_parameters.csv
| | | |---- fit_errors.txt
| | |---- 02_analyses/
| | | |---- figures/
| | | |---- repeatability/
| | | |---- reproducibility
| | | |---- benchmarks.csv
| | | |---- mixed_anova.csv
For reference, the results and figures presented in the accompanying manuscript
were created using the outputs contained in results/SixTestCompounds/2023-04-17.
The script rat.py contains the TRISTAN-rat model used for dynamic
gadoxetate-enhanced MRI in rats. Within this script, all tracer
kinetic modelling equations and associated default variables used
in the preclinical dynamic gadoxetate-enhanced MR imaging work of
the IMI-TRISTAN WP2 project are defined. The interested user may employ
the funtions in this script to develop their own analysis workflow using
their own datasets, by creating similar scripts to those shown in the
TRISTAN-rat src directory.
The models outlined in rat.py may be used to fit liver and spleen region
of interest (ROI) time curve data provided by the user. This data must be
provided in a csv format to match the data contained inside the
data/SixTestCompounds/01_signals subfolder. Here, each csv file contains the
liver and spleen ROI time curve data for a single rat at a specific site and
day, after administration of the test compound of interest.
An additional Jupyter notebook TristanRat_examples.ipynb has been provided
for interacting with some of the functions and methods of the TristanRat class
described in the src/rat.py script.
PLEASE NOTE: The functions developed in this notebook are examples only to demonstate how the functions and methods defined in
rat.pymay be used for simulations and for fitting DCE-MRI datasets. It is up to the user to build their own functions in a similar manner shown in the examples to employ the TRISTAN-rat model in their own analysis workflow.
To try some of these examples, run the command:
jupyter notebook
This will start a server and open the Jupyter interface within the operating
system's default browser. In the contents, navigate to the notebook
notebooks/TristanRat_examples.ipynb and select to open.
Each example in the notebook is provided in a separate cell. Pressing
Shift + Enter on the keyboard executes the code within a cell and prints the
output below. Feel free to interact with the examples by adjusting the code and
exploring the different outputs obtained as a result.
| Name | GitHub Handle | |
|---|---|---|
| Ebony R. Gunwhy | @EbonyGunwhy | [email protected] |
| Steven Sourbron | @plaresmedima | [email protected] |
The research leading to these results received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 116106. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
1. Melillo N, Scotcher D, Kenna JG, Green C, Hines CDG, Laitinen I, et al. Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug–Drug Interactions in Rats. Pharmaceutics 2023;15(3):896. doi: 10.3390/pharmaceutics15030896