基于质谱的脂质组学和代谢组学会产生海量数据集，这些数据集连同临床参数等元数据，需要特定的数据探索技能，才能识别并以可视化方式呈现统计学上显著的趋势和生物学上有意义的差异。除了各个实验室自行开发的定制方法之外，还存在一套坚实且可自由获取的探索性数据分析与可视化工具，我们在此将其汇编成册，内容包括描述性统计的编制、带注释的箱线图、假设检验、火山图、脂质图谱和脂肪酰基链图、无监督与有监督降维、树状图和热图。本综述面向希望利用免费可用的 R 或 Python 方案提升数据分析与可视化技能的读者。初学者可以在引导下选用一系列 R 和 Python 库，制作达到发表质量的图形，而不被代码复杂性所困扰。本稿件连同载有分步说明的配套 GitBook 代码仓库，为读者提供了全面指南，鼓励将 R 与 Python 应用于稳健且可复现的……
Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods developed by individual labs, a solid core of freely accessible tools exists for exploratory data analysis and visualization, which we have compiled here, including preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps and fatty acyl chain plots, unsupervised and supervised dimensionality reduction, dendrograms, and heat maps. This review is intended for those who would like to develop their skills in data analysis and visualization using freely available R or Python solutions. Beginners are guided through a selection of R and Python libraries for producing publication-ready graphics without being overwhelmed by the code complexity. This manuscript, along with associated GitBook code repository containing step-by-step instructions, offers readers a comprehensive guide, encouraging the application of R and Python for robust and reproduc…