grandpy-lib 0.1.0

Module for kinetic RNA-seq analysis.

GrandPy

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. GrandPy provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data. It mimics the core functionality of the original grandR package, by which it is inspired.

Installation

GrandPy can be installed using the following command:

 pip install grandpy-lib

You can also install the development version directly from GitHub:

 pip install git+https://github.com/maus2310/GrandPy 

System Requirements

GrandPy has mostly been tested on Windows but should also run on Linux and macOS. The package runs on standard laptops (multicore CPUs are recommended; memory requirements depend on the size of your datasets).

Requires python 3.12.0

Installing it via pip will make sure that the following (standard) packages are available:

numpy, pandas, scipy, anndata, tqdm, matplotlib, seaborn, scanpy, pydeseq2

Additional packages are optional and important for particular functions:

scikit-learn, mygene, numdifftools

Cheatsheet

How to get started

First, have a look at the getting started notebook.

Next, explore differential expression or kinetic modeling, which provide an overview of the two primary settings for nucleotide conversion experiments.

There are also additional notebooks:

• Loading data and working with GrandPy objects: Learn more about programming with GrandPy
• Working with data matrices and analysis results: Learn more about how to retrieve data from GrandPy objects
• Plotting: Learn how to create and store plots with GrandPy
• Pulse-chase: Learn how to fit pulse-chase data with GrandPy

Acknowledgements

GrandPy is heavily inspired by the grandR R package by Prof. Dr. Florian Erhard, Teresa Rummel, Lygeri Sakellaridi and Kevin Berg. We gratefully acknowledge their work, as well as the efforts of the team behind it, especially Julian Selke and Rahaf Issa, whose contributions made GrandPy possible.