The pIMZ framework: installation and usage

This section will tell you how to install pIMZ, but also refer you to some examples on how to use pIMZ.

Installation

The easiest way to install most Python packages is via pip.

If not already done, you must first install the following dependencies manually. This is because at the time of writing this document, dabest requires pandas~=0.25, which is incompatible with probably the rest of the world nowadays. So first dabest is installed, then numpy and pandas are upgraded again

sudo pip3 install dabest
sudo pip3 install numpy pandas --upgrade

Only then we should install pIMZ

sudo pip3 install pIMZ

pIMZ is now ready to go!

Usage

pIMZ can be used with several use-cases. Here two of these are highlighted.

  1. Using a local imzML-file as source

  2. Using public HuBMap-Data

Local imzML-file

The usage of pIMZ on a local imzML is shown in the examples. The IMZMLprocess notebook showcases how to use pIMZ to retrieve the correct data, normalize intensities, cluster spectra, determine background, perform differential analysis and finally combine multiple region for further comparative analyses.

Public HuBMap-Data

The HuBMap-Data example is divided into two notebooks. The first notebook describes the use of the HuBMap-downloader. Since HuBMap-files are stored on Globus, specific care to access their API has to be taken. In order to download files yourself, you have to run Globus Connect Personal on the machine where you want to download the data to.

The usage of pIMZ on a public HuBMap-generated tissue is shown in the examples. The HuBMAP_kidney notebook showcases how to use pIMZ to retrieve the correct data, normalize intensities, cluster spectra, determine background, perform differential analysis and finally combine multiple region for further comparative analyses. In general, the workflow is no different that for any other local data.