University of Clermont Auvergne, CNRS, LMBP, Clermont-Ferrand, France.
University of Clermont Auvergne, INRAE, VetAgro Sup, UMRF, Aurillac, France.
Sci Rep. 2024 Nov 16;14(1):28257. doi: 10.1038/s41598-024-79913-y.
Caenorhabditis elegans is highly important in current research, serving as a pivotal model organism that has greatly advanced the understanding of fundamental biological processes such as development, cellular biology, and neurobiology, helping to promote major advances in various fields of science. In this context, the survival of a nematode under various conditions is commonly investigated via statistical survival analysis, which is typically based on hypothesis testing, providing valuable insights into the factors influencing its longevity and response to various environmental factors. The extensive reliance on hypothesis testing is acknowledged as a concern in the scientific analysis process, emphasizing the need for a comprehensive evaluation of alternative statistical approaches to ensure a rigorous and unbiased interpretation of research findings. In this work, we propose an alternative method to hypothesis testing for evaluating differences in nematode survival. Our approach relies on a clustering technique that takes into account the complete structure of survival curves, enabling a more comprehensive assessment of survival dynamics. The proposed methodology helps to identify complex effects on nematode survival and enables us to derive the probability that treatment induces a specific effect. To highlight the application and benefits of the proposed methodology, it is applied to two different datasets, one simple and one more complex.
秀丽隐杆线虫在当前的研究中具有重要意义，作为一种关键的模式生物，它极大地促进了对发育、细胞生物学和神经生物学等基本生物学过程的理解，并推动了科学的多个领域的重大进展。在这种背景下，人们通常通过统计生存分析来研究线虫在各种条件下的生存情况，该分析通常基于假设检验，为影响其寿命和对各种环境因素的反应的因素提供了有价值的见解。在科学分析过程中，广泛依赖假设检验被认为是一个关注点，强调需要对替代统计方法进行全面评估，以确保对研究结果进行严格和无偏的解释。在这项工作中，我们提出了一种替代假设检验的方法来评估线虫生存差异。我们的方法依赖于一种聚类技术，该技术考虑了生存曲线的完整结构，从而能够更全面地评估生存动态。所提出的方法有助于识别对线虫生存的复杂影响，并使我们能够得出处理诱导特定效果的概率。为了突出所提出方法的应用和优势，将其应用于两个不同的数据集，一个简单，一个更复杂。
