College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, 310000, China.
J Mol Biol. 2020 May 15;432(11):3411-3421. doi: 10.1016/j.jmb.2020.01.027. Epub 2020 Feb 7.
Comparative biological studies typically require plenty of samples to ensure full representation of the given problem. A frequently-encountered question is how many samples are sufficient for a particular study. This question is traditionally assessed using the statistical power, but it alone may not guarantee the full and reproducible discovery of features truly discriminating biological groups. Two new types of statistical criteria have thus been introduced to assess sample sufficiency from different perspectives by considering diagnostic accuracy and robustness. Due to the complementary nature of these criteria, a comprehensive evaluation based on all criteria is necessary for achieving a more accurate assessment. However, no such tool is available yet. Herein, an online tool SSizer (https://idrblab.org/ssizer/) was developed and validated to enable the assessment of the sample sufficiency for a user-input biological dataset, and three statistical criteria were adopted to achieve comprehensive and collective assessment. A sample simulation based on a user-input dataset was performed to expand the data and then determine the sample size required by the particular study. In sum, SSizer is unique for its ability to comprehensively evaluate whether the sample size is sufficient and determine the required number of samples for the user-input dataset, which, therefore, facilitates the comparative and OMIC-based biological studies.
比较生物学研究通常需要大量的样本，以确保充分代表给定的问题。一个经常遇到的问题是，对于特定的研究，需要多少个样本。这个问题通常是通过统计功效来评估的，但它本身并不能保证真正区分生物群体的特征的完全和可重复的发现。因此，引入了两种新的统计标准，从不同的角度通过考虑诊断准确性和稳健性来评估样本的充分性。由于这些标准具有互补性，因此需要基于所有标准进行全面评估，才能实现更准确的评估。然而，目前还没有这样的工具。在这里，开发并验证了一个在线工具 SSizer（https://idrblab.org/ssizer/），以评估用户输入的生物数据集的样本充分性，并采用了三个统计标准来实现全面和综合的评估。基于用户输入的数据集进行了样本模拟，以扩展数据，然后确定特定研究所需的样本量。总的来说，SSizer 的独特之处在于它能够全面评估样本量是否充足，并确定用户输入数据集所需的样本数量，从而促进了基于比较和 OMIC 的生物学研究。
