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. 2020 Sep;2(3):lqaa052.
doi: 10.1093/nargab/lqaa052. Epub 2020 Jul 29.

Comparison of visualization tools for single-cell RNAseq data

Batuhan Cakir  1 , Martin Prete  1 , Ni Huang  1 , Stijn van Dongen  1 , Pinar Pir  2 , Vladimir Yu Kiselev  1
Affiliations

Comparison of visualization tools for single-cell RNAseq data

Batuhan Cakir et al. NAR Genom Bioinform. 2020 Sep.

Abstract

In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a single cell to millions of cells. Due to its high dimensionality, it is not always feasible to visualize scRNAseq data and share it in a scientific report or an article publication format. Recently, many interactive analysis and visualization tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review several of the currently available scRNAseq visualization tools and benchmark the subset that allows to visualize the data on the web and share it with others. We consider the memory and time required to prepare datasets for sharing as the number of cells increases, and additionally review the user experience and features available in the web interface. To address the problem of format compatibility 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 visualization.

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Figures

Figure 1.
Figure 1.
Preprocessing RAM usage (A) and preprocessing times (B) of the visualization tools. The points on the plots represent mean values and error bars represent standard error across five independent runs. Preprocessing times include input preparation and starting of the back-end server.
See this image and copyright information in PMC

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