Dimensionality reduction techniques are essential in analyzing large ‘omics’ datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as “black boxes”, and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.

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揭秘“组学”时代的降维技术：生物科学专业学生的实用方法

降维技术对于分析生物化学和分子生物学中的大型“组学”数据集至关重要。主成分分析、t 分布随机邻域嵌入以及均匀流形逼近和投影通常用于数据可视化。然而，这些方法对于没有强大数学背景的学生来说可能具有挑战性。在本研究中，使用 COVID-19 数据创建了直观的示例，以帮助学生理解这些技术背后的核心概念。在为期 4 小时的实践课程中，我们使用这些示例向 15 名生物医学背景的研究生演示了降维技术。使用 Python 和 Jupyter 笔记本，我们的目标是揭开这些通常被视为“黑匣子”的方法的神秘面纱，并使学生能够生成和解释自己的结果。为了评估我们方法的影响，我们进行了一项匿名调查。大多数学生认为使用计算机丰富了他们的学习体验 (67%)，并且 Jupyter 笔记本是课堂上很有价值的一部分 (66%)。此外，60% 的学生表示对 Python 的兴趣增加，40% 的学生对降维方法产生了兴趣并有了更好的理解。尽管课程持续时间较短，但 40% 的学生表示获得了该领域所需的研究技能。虽然需要进一步分析这种方法的学习影响，但我们相信分享我们生成的示例可以为其他人在交互式教学环境中使用提供宝贵的资源。这些例子以易于理解的方式突出了现代生物信息学分析中使用的主要降维方法的优点和局限性。