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Use to Effectively Utilize Plotting Space to Deal With Large Datasets and Outliers.

作者信息

Xu Shuangbin, Chen Meijun, Feng Tingze, Zhan Li, Zhou Lang, Yu Guangchuang

机构信息

Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.

出版信息

Front Genet. 2021 Nov 2;12:774846. doi: 10.3389/fgene.2021.774846. eCollection 2021.


DOI:10.3389/fgene.2021.774846
PMID:34795698
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8593043/
Abstract

With the rapid increase of large-scale datasets, biomedical data visualization is facing challenges. The data may be large, have different orders of magnitude, contain extreme values, and the data distribution is not clear. Here we present an R package that allows users to create broken axes using syntax. It can effectively use the plotting area to deal with large datasets (especially for long sequential data), data with different magnitudes, and contain outliers. The package increases the available visual space for a better presentation of the data and detailed annotation, thus improves our ability to interpret the data. The package is fully compatible with and it is easy to superpose additional layers and applies scale and theme to adjust the plot using the syntax. The package is open-source software released under the Artistic-2.0 license, and it is freely available on CRAN (https://CRAN.R-project.org/package=ggbreak) and Github (https://github.com/YuLab-SMU/ggbreak).

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/8c128173ba45/fgene-12-774846-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/23a0cfea52b8/fgene-12-774846-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/deac4e0f76e0/fgene-12-774846-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/4c68c50f5e39/fgene-12-774846-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/608a29b52197/fgene-12-774846-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/8c128173ba45/fgene-12-774846-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/23a0cfea52b8/fgene-12-774846-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/deac4e0f76e0/fgene-12-774846-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/4c68c50f5e39/fgene-12-774846-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/608a29b52197/fgene-12-774846-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3afd/8593043/8c128173ba45/fgene-12-774846-g005.jpg

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本文引用的文献

[1]
Genome-wide association studies and heritability analysis reveal the involvement of host genetics in the Japanese gut microbiota.

Commun Biol. 2020-11-18

[2]
Effects of TiO nanoparticles on intestinal microbial composition of silkworm, Bombyx mori.

Sci Total Environ. 2019-11-24

[3]
The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.

Nucleic Acids Res. 2019-1-8

[4]
Log-transformation and its implications for data analysis.

Shanghai Arch Psychiatry. 2014-4

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Sequence and phylogenetic analysis of the gp200 protein of Ehrlichia canis from dogs in Taiwan.

J Vet Sci. 2010-12

[6]
ExPASy: The proteomics server for in-depth protein knowledge and analysis.

Nucleic Acids Res. 2003-7-1

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The box plot: a simple visual method to interpret data.

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