NKI-CCB / imagene-analysis

Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression

Integration of MRI and RNAseq data in the Imagene Project

Integration of MRI and RNAseq data of the Imagene project. Paper published in Radiology: "Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression." https://doi.org/10.1148/radiol.2020191453.

To make a virtual environment for R and Python usage:

make requirements

Then you can process the data, train models and run analyses:

make all

Remote files

Data is not included in this repository. The download method and location of the data can be specified in the config/snakemake.yaml file.

Pathway Analysis Requirements

The pathway analyses requires the msigdb files release 5.2. These cannot be downloaded automatically, as registration is required. Before running the pathway analysis, download msigdb_v5.2_files_to_download_locally.zip from the msigdb site under Archived Releases and place it into data/external/msigdb. The scripts will extract the required files from the archive.

Project Organization

├── LICENSE
│
├── Makefile           <- Makefile with commands like `make all` and `make venv`.
│
├── Snakefile          <- Snakemake file with rules to run this project. These rules
│                         should be run inside a virtual environment containing the packages
│                         in requirements.txt (Python) and requirement.R (R). The Makefile can
│                         set this environment up on some systems.
│
├── README.md          <- The top-level README for developers using this project.
│
├── docs               <- Project Documentation using Sphinx.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── config/snakemake.yaml <- Configuration of the workflow. Set version of Python and R used here.
│
├── data
│   ├── raw            <- The original, immutable data dump.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   ├── external       <- Data from third party sources, such as reference data.
│   └── to_share       <- Data for third parties.
│
├── notebooks          <- Jupyter notebooks for exploration. Naming convention is date (for
│                         ordering), the creator's initials, and a short `-` delimited
│                         description, e.g. `2017-01-01-tb-initial-data-exploration`.
│
├── models             <- Trained models and model predictions.
│
├── analyses           <- Results and summaries of statistical analyses.
│
├── reports            <- Analysis reports, such as pweave or rmarkdown reports.
│   └── figures        <- Generated graphics and figures to be used in reporting.
├── figures            <- Figures for in the manuscript.
│
├── venv/              <- Suggested directory for virtual environment
│
├── requirements.txt   <- The Python requirements file for reproducing the analysis
│                         environment e.g. generated with `pip freeze > requirements.txt`.
├── requirements.R     <- Requirements and installation script for R.
│
└── src                <- Source code for use in this project.
    │
    ├── plot.py        <- Plotting library.
    ├── util.py        <- Utility function library.
    │
    ├── analysis       <- Scripts for statistical analysis of data or models.
    │
    ├── data           <- Scripts to download or generate data.
    │
    ├── features       <- Scripts to construct features from data.
    │
    ├── models         <- Scripts to train and apply models.
    │
    ├── reports        <- Source of reports, such as snippets used in multiple reports.
    │
    └── visualization  <- Scripts to visualize results.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience