树筛:一个用于查看系统发育树和数据的R包和服务器

A. Wright
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引用次数: 1

摘要

树筛是一个Shiny(Chang,Cheng,Allaire,Xie,&McPherson,2018)应用程序,用于可视化系统发育树和用于估计它们的基础数据之间的关系。它也可以在RStudio中使用(RStudioTeam,2015)或在命令行中作为R包使用(R Core Team,2013)。树筛的工作原理是根据用户提供的关于要可视化的字符的输入,对系统发育矩阵进行子集设置。然后根据每个数据子集来估计最大简约树。选择最大简约是为了速度和分析的简单性。在简约最优性标准下,优选树是指在进化史上进化步骤最少或特征变化最少的树。该树在简约性和Lewis Mk模型(Lewis,2001)下进行评分,Lewis Mk是一种根据离散特征数据估计系统发育的最大似然模型。然后,基于ggplot2(Wickham,2016)包,使用ggtree(Yu,Smith,Zhu,Guan,&Lam,2017)对数据和树进行可视化。无论学习者是通过GUI还是RStudio界面进行交互,预期输出都是相同的;然而,RStudio界面确实有GUI中没有的其他选项。
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treesiftr: An R package and server for viewing phylogenetic trees and data
treesiftr is a Shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2018) application for visualizing the relationship between phylogenetic trees and the underlying data used to estimate them. It can also be used in RStudio (RStudio Team, 2015) or at the command line as an R package (R Core Team, 2013). treesiftr works by subsetting a phylogenetic matrix according to user-provided input about which characters to visualize. A maximum parsimony tree is then estimated from each data subset. Maximum parsimony was chosen for speed and analytical simplicity. Under the parsimony optimality criterion, the preferred tree is the one that suggests the fewest evolutionary steps, or character changes over evolutionary history. The tree is scored under both parsimony and Lewis’ Mk model (Lewis, 2001), a maximum likelihood model for estimating phylogeny from discrete character data. The data and tree are then visualized using ggtree (Yu, Smith, Zhu, Guan, & Lam, 2017), based upon the ggplot2 (Wickham, 2016) package. Expected outputs are the same whether the learner is interacting via the GUI or the RStudio interface; however, the RStudio interface does have additional options not available in the GUI.
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