Abstract
Single Cell transcriptomic analysis has become a widespread technology of choice when it comes to understanding the differences at a transcriptomic level in heterogeneous samples. As a consequence, a plethora of analysis tools have been published to tackle the different analysis steps from count matrix generation to downstream analysis. Many of them provide ways to generate visualizations of the data. While some design choices are made, it is a common practice to provide the user with visualizations as raw as possible so that they can be customized to the user needs. However, in many cases these final customization steps are either time consuming or demand a very specific set of skills. This problem is addressed by SCpubr, which sacrifices some of this initial freedom of choice in aesthetics to provide the user a more streamlined way of generating high quality Single Cell transcriptomic visualizations.
Competing Interest Statement
The authors have declared no competing interest.
