Logistic-Regression-for-Biologists-main.

The choice of appropriate statistical tests in experimental biology is critical for scientific rigor and can be challenging in the case of categorical data analysis. Using example datasets from Caenorhabditis elegans research, we conduct statistical analysis of (1) a rare cellular event involving the formation of a neuronal extrusion called an exopher and (2) a variable behavioral response across time. We employ the Cochran–Mantel–Haenszel (CMH) test and logistic regression for analysis. Recognizing there are potential accessibility issues using logistic regression, we provide step-by-step tutorials and example code. We emphasize that logistic regression can handle both simple and complex multivariable datasets; logistic regression can also provide more comprehensive insights into experimental outcomes when compared to simpler tests like CMH. By analyzing real biological examples and demonstrating their analysis with R code, we provide a practical guide for biologists to enhance the rigor and reproducibility of categorical data analysis in experimental studies.

在实验生物学研究中，选择恰当的统计检验方法对科研严谨性至关重要，而在开展分类数据分析时，这一选择往往颇具挑战性。我们以秀丽隐杆线虫（Caenorhabditis elegans）研究中的示例数据集为基础，针对两类对象开展统计分析：(1) 一类罕见的细胞事件——涉及名为外突体（exopher）的神经元外突结构的形成；(2) 随时间变化的可变行为应答。我们采用科克伦-曼特尔-亨塞尔检验（Cochran–Mantel–Haenszel, CMH）与逻辑回归（logistic regression）开展分析。考虑到逻辑回归的使用可能存在可及性障碍，我们提供了分步教程与示例代码。我们强调，逻辑回归既可处理简单数据集，也可处理复杂的多变量数据集；相较于CMH这类更为简易的检验方法，逻辑回归还能为实验结果提供更全面的解读视角。我们通过分析真实生物学案例，并辅以R代码演示分析流程，为生物学家提供了一份实用指南，以提升实验研究中分类数据分析的严谨性与可重复性。