Analyses for the Molecular Oncology Almanac publication. Some common code used by multiple notebooks, mostly style preferences for figures, can be found in common/ and all figures produced in this repository can be found in figures/.
Code in this repository uses Python 3.7 and there is one R script in analyses/knowledge-bases/pmid-comparison/. The R script contains the package installation. For Python, we recommend using a virtual environment and running Python with either Anaconda or Miniconda. After installing Anaconda or Miniconda, you can set up by running
conda create -y -n moalmanac-paper python=3.7
conda activate moalmanac-paper
pip install -r requirements.txt
ipython kernel install --user --name=moalmanac-paperSeveral notebooks in this repository change the default font to Arial. The following command is used to install Arial as a font option for matplotlib,
conda install -n moalmanac-paper -c conda-forge mscorefonts
Afterwards, you will have to edit your matplotlibrc file for your jupyter notebook to uncomment line 207 and change Arial to the first item. For me on a macbook pro, this file was located here: /Users/brendan/opt/miniconda3/envs/moalmanac-paper/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc. This guide from the fowler lab was used to change font preferences with matplotlib.
Please cite our paper if using any information or code from this repository
You can also see prior iterations of this work from AACR abstracts over the years,
- AACR 2019, Abstract 2470: A molecular oncology almanac for integrative clinical interpretation of molecular profiles to guide precision cancer medicine
- AACR 2018, Abstract 2286: Feature-based clinical interpretation of whole exome and transcriptome data for precision cancer medicine
- AACR 2017, Abstract 558: Computational analysis of clinically actionable genomic features: precision heuristics for interpreting the alteration landscape (PHIAL) with the associated github repo.