This repository contains the necessary data, python source code and jupyter notebooks to reproduce the results from our manuscript, "Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes." Using in vitro data to estimate point of departure (POD) values is an important component of new approach method (NAM)-based chemical risk assessments. In this case study we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI) and in vitro to in vivo extrapolation (IVIVE).
├── README.md # This file
├── LICENSE # License file
├── Makefile # Makefile to automate most steps
├── environment # Python an R environment setup
│ ├── condaenv.yml
│ ├── rdeps.csv
│ └── requirements.txt
│
├── inputs # Input data for the analysis
│ ├── cyprotex-heprn2-jul-2017-raw-v2.csv # Raw well level HCI data
│ ├── heprn-chems-1.tsv # Chemical information
│ ├── heprn-toxcast-hits-qual.csv # ToxCast in vitro assay data
│ └── toxref-v2.0-pods-rat-liver.xlsx # ToxRefDB in vivo effect data
│
├── notebooks # Jupyter notebooks for the workflow
│ └── heprn-ivive # This paper
│ ├── 010-heprn-db.ipynb # Build the database
│ ├── 012-heprn-chm.ipynb # Load chemical information
│ ├── 015-heprn-raw.ipynb # Load the raw HCI data
│ ├── 050-heprn-qc.ipynb # Run HCI QC
│ ├── 100-heprn-fc.ipynb # Analyze HCI effects
│ ├── 110-heprn-cr.ipynb # Analyze concentration response
│ ├── 130-heprn-cr-manual-qc.ipynb # Interactive manual QC
│ ├── 152-invitro-potency.ipynb # Summarise all in vitro bioactivity data
│ ├── 205-qivive-rtk.ipynb # PBTK simulation for all chemicals
│ ├── 223-heprn-qivive-aed-cmp.ipynb # IVIVE using rTK
│ └── 301-heprn-qivive-figs.ipynb # Generate all figures, tables, suppl. mat.
│
├── outputs # All output files
│ ├── figs
│ └── suppl
├── setup.py
│
├── src # All python packages to support workflow
│ └── heprnhci
│ ├── cr # concentration-response analysis
│ ├── db # MongoDB database creation
│ ├── hci # hci processing
│ ├── tk # toxicokinetic modeling
│ ├── utl
│ └── viz # visualisation
│
└── test_environment.py
Install miniconda3 by following these instructions. Make sure that the miniconda3/bin directory is in your path.
Follow these instructions to install Jupyterlab
All the Python3 and R dependencies can be installed using the files in the environments directory. These instructions assume that all dependencies will be installed in a conda environment. First, create the conda environment using the following shell command:
conda env create -f environment/condaenv.yml -n hcinam
If all goes well then switch to the new environment using: conda activate hcinam. Next, install the R packages after starting the session (make sure you're using the hcinam environment):
new_pkgs <- read.csv('environment/rdeps.csv') existing_pkgs <- as.data.frame(installed.packages()) to_install <- setdiff(existing_pkgs,new_pks) install.packages(to_install)
Create a .env file in the notebooks directory with the following information (path_to is the path to the top level directory in which this repo was cloned):-
TOP=/path_to/heprn-hci-nam/ LIB=/path_to/heprn-hci-nam/src DAT_DIR=/path_to/heprn-hci-nam/inputs/ SUP_DIR=/path_to/heprn-hci-nam/v2.0-supp/ FIG_DIR=/path_to/heprn-hci-nam/figs/v2.0/' R_HOME=/path_to_miniconda_envs/hcinam/lib/R MONGO_HCIDB=name of the mongodb database MONGO_HOST= hostname for the mongodb server
All raw and processed HCI data are stored in a MongoDB database. This database can be build using data from the inputs directory or downloaded from this ftp site.
Follow instructions here to install MongoDB for your operating system. Once you have started MongoDB on your system you will need to create a database.
All figures and tables can be reproduced by running this notebook in Jupyterlab:
notebooks/heprn-ivive/301-heprn-qivive-figs.ipynb
With help from the cookiecutter data science project template. #cookiecutterdatascience