phenotools: an R package for visualizing and analyzing phenomic datasets

1.Phenotypic data is crucial for understanding genotype–phenotype relationships, assessing the tree of life, and revealing trends in trait diversity over time. Large‐scale description of whole organisms for quantitative analyses (phenomics) presents several challenges, and technological advances in the collection of genomic data outpace those for phenomic data. Reasons for this disparity include the time‐consuming and expensive nature of collecting discrete phenotypic data and mining previously‐published data on a given species (both often requiring anatomical expertise across taxa), and computational challenges involved with analyzing high‐dimensional datasets.

2.One approach to building approximations of organismal phenomes is to combine published datasets of discrete characters assembled for phylogenetic analyses into a phenomic dataset. Despite a wealth of legacy datasets in the literature for many groups, relatively few methods exist for automating the assembly, analysis, and vi...

1. 表型数据（phenotypic data）对于理解基因型-表型关系（genotype–phenotype relationships）、评估生命之树（tree of life）以及揭示性状多样性随时间的变化趋势至关重要。为定量分析而对整个生物体进行大规模描述（表型组学，phenomics）面临若干挑战，且基因组数据（genomic data）采集的技术进步速度远超表型组数据（phenomic data）。造成这种差距的原因包括：采集离散表型数据（discrete phenotypic data）以及挖掘特定物种已发表数据的过程耗时且成本高昂（两者通常均需跨类群的解剖学专业知识），此外还有分析高维数据集（high-dimensional datasets）所涉及的计算挑战。

2. 构建生物体表型组（organismal phenomes）近似模型的一种方法是，将为系统发育分析（phylogenetic analyses）而汇集的已发表离散性状数据集整合为一个表型组数据集（phenomic dataset）。