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Comparison of visualisation tools for single-cell RNAseq data

Batuhan Çakır, Martin Prete, Ni Huang, Stijn van Dongen, Pınar Pir, Vladimir Yu. Kiselev
doi: https://doi.org/10.1101/2020.01.24.918342
Batuhan Çakır
Wellcome Sanger Institute, Hinxton, UKGebze Technical University, Gebze, Kocaeli, Turkey
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Martin Prete
Wellcome Sanger Institute, Hinxton, UK
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Ni Huang
Wellcome Sanger Institute, Hinxton, UK
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Stijn van Dongen
Wellcome Sanger Institute, Hinxton, UK
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Pınar Pir
Gebze Technical University, Gebze, Kocaeli, Turkey
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Vladimir Yu. Kiselev
Wellcome Sanger Institute, Hinxton, UK
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Abstract

In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.

Footnotes

  • https://github.com/cellgeni/sceasy

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 4.0 International license.
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Posted January 25, 2020.
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Comparison of visualisation tools for single-cell RNAseq data
Batuhan Çakır, Martin Prete, Ni Huang, Stijn van Dongen, Pınar Pir, Vladimir Yu. Kiselev
bioRxiv 2020.01.24.918342; doi: https://doi.org/10.1101/2020.01.24.918342
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Comparison of visualisation tools for single-cell RNAseq data
Batuhan Çakır, Martin Prete, Ni Huang, Stijn van Dongen, Pınar Pir, Vladimir Yu. Kiselev
bioRxiv 2020.01.24.918342; doi: https://doi.org/10.1101/2020.01.24.918342

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