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Generating publication ready visualizations for Single Cell transcriptomics using SCpubr

View ORCID ProfileEnrique Blanco-Carmona
doi: https://doi.org/10.1101/2022.02.28.482303
Enrique Blanco-Carmona
1Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany
2Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
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  • ORCID record for Enrique Blanco-Carmona
  • For correspondence: e.blancocarmona@kitz-heidelberg.de
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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.

Footnotes

  • https://github.com/enblacar/SCpubr

  • https://enblacar.github.io/SCpubr-book/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 01, 2022.
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Generating publication ready visualizations for Single Cell transcriptomics using SCpubr
Enrique Blanco-Carmona
bioRxiv 2022.02.28.482303; doi: https://doi.org/10.1101/2022.02.28.482303
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Generating publication ready visualizations for Single Cell transcriptomics using SCpubr
Enrique Blanco-Carmona
bioRxiv 2022.02.28.482303; doi: https://doi.org/10.1101/2022.02.28.482303

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