Application of R language graphics in biological research
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摘要: R语言具有强大的数据分析处理和可视化绘图功能，可以在Window、Linux以及Mac系统上使用，并且根据其编写新代码或调整已有代码可轻松实现科研中数据呈现与图形绘制的要求.然而其代码学习较为艰难、R-package使用复杂，所以未受到科研新手的青睐.基于生物科学相关领域的背景，整理汇集已出版的文献、公共数据库以及国家统计局中提供的数据，使用R语言、R编辑器RStudio并载入相关的R-package绘制出地图、热图、关联网络图、韦恩图和柱形图等高质量的图片，并提供相应的脚本与说明，以方便生物科研人员直接更改使用.本研究对生物科研中合理、直观地表述研究结果提供了良好的范例，并进行了详细讨论，且与其他绘图软件作了比较，以期R语言能够成为生物科学领域科研工作者入门学习、研究应用中绘制图片的首选工具.Abstract: The R programming language offers powerful statistical analysis, data processing, and visualization capabilities, which can run on Windows, Linux, and Mac operating systems; the software allows users to show scientific research data through graphics by writing new code or adjusting existing code. However, it is difficult to learn R's programming code and use R packages, so R is not commonly favored by novices in the scientific research community. Based on the context of bioscience fields and data from published papers, public databases, and the National Bureau of Statistics, this study demonstrates how to plot high quality maps, heat maps, functional networks, Venn diagrams, and column charts using R, R Studio, and R-packages. To facilitate the use of R, we offer detailed scripts for biological researchers. After comparing R with other graphing software, this study provides good examples and discussions of how to reasonably and intuitively present results in biological research. We hope R will become the preferred plotting tool for elementary biological researchers in their learning and research activities.
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Key words:
- map /
- heat map /
- functional network /
- Venn diagram /
- column chart /
- R language
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图 1 近10年33个省份西瓜平均产量
Fig. 1 The average yield of watermelon crops across 33 provinces in the last 10 years
图 2 生物胁迫RNA-seq在一天中3个时刻的表达量的变化
Fig. 2 RNA-seq data showing subsets of genes for biotic stress at three instances per-day
图 4 在不同的储存条件下黑腹果蝇的静止幼虫显著上调的mRNA转录物的数量
Fig. 4 The number of significantly up-regulated mRNA transcripts in quiescent larvae of Drosophila melanogaster stored at different conditions compared to the start of the experiment
表 1 10种常用R-package信息介绍
Tab. 1 Introduction to common R-packages
序号 R-package 作用特点 开发时间 参考文献 1 ggplot2 使用图形语法创建数据可视化 2013年10月 [29] 2 lattice 复合图像组合图形 2008年 [30] 3 VennDiagram 绘制Venn图 2011年1月 [31] 4 Metacoder 群体数据分类并以树形式可视化显示 2017年2月 [32] 5 treeman 高效和直观地构建系统发育树 2017年1月 [33] 6 DGCA 差异基因分析 2016年11月 [34] 7 vcfr 操作和可视化VCF数据 2017年1月 [35] 8 sfinx 消除亲和纯化-质谱数据集中的假阳性 2017年2月 [36] 9 snpGeneSets 基因组学研究注释 2016年12月 [37] 10 Gogadget GO富集结果的分析和可视化 2016年12月 [38] 
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