If you want to use a Jupyter notebook, you can launch it with: jupyter notebook Then activate our new conda environment: conda activate msstats-demo Let’s start by creating the conda environment from this file: conda env create -f

matplotlib # The de facto standard Python plotting library bioconductor-msstats=4.2.0 # The MSstats R package I’ve created a conda environment YAML file that looks like this: # 2 First, we’ll create a new conda environment, msstats-demo, and install the necessary packages from the bioconda and conda-forge channels. I use conda as my package manager for Python and R whenever possible. If you want to follow along with this post, you’ll need to install a few things.

This entire post is available as a Jupyter notebook on GitHub: Setup I’ll be using proteomics data as an example, but understanding it is not critical for learning from this post.

1 Additionally, I’ll assume that you have some familiarity with R programming, since you’re reading this post. This post will assume that you’re comfortable programming in Python and that you’re familiar with the Pandas, NumPy, and Matplotlib Python packages. In this post I’ll show you how I use the MSstats R package from within a Python script or Jupyter notebook, providing an example for how you can use the occasional R package in your own analyses. For me this is most often the excellent MSstats R package from Meena Choi ( and folks in Olga Vitek’s lab ( Although it has always been possible to write R scripts alongside my Python scripts and Jupyter notebooks, I find it cumbersome to switch back and forth between them, particularly when it involves generating unnecessary intermediate files. However, I sometimes find myself needing to use an R package, such as those provided by Bioconductor. How to Use R Packages in Python ~10 MIN READĪs a data scientist working with biological data, the programming language I use on a daily basis is Python.

Packages in python