Department of Statistics, University of Georgia, 101 Cedar St, Athens, GA, 30602, USA.
Department of Biological Sciences, Purdue University, 915 W. State Street, West Lafayette, IN, 47907, USA.
Sci Rep. 2017 Jun 7;7(1):2937. doi: 10.1038/s41598-017-02822-w.
Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour. The features of these data, however, present a challenge to traditional statistical tests. For example, many larvae display little or no movement. Thus, the larval responses have many zero values and are imbalanced. These responses are also measured repeatedly from the same well, which results in correlated observations. These analytical issues were addressed in this study by the generalized linear mixed model (GLMM). This approach deals with binary responses and characterizes the correlation of observations in the same group. It was used to analyze a previously reported dataset. Before applying the GLMM, the activity values were transformed to binary responses (movement vs. no movement) to reduce data imbalance. Moreover, the GLMM estimated the variations among the effects of different well locations, which would eliminate the location effects when two biological groups or conditions were compared. By addressing the data-imbalance and location-correlation issues, the GLMM effectively quantified true biological effects on zebrafish locomotor response.
在环境光照发生剧烈变化时，斑马鱼幼虫会表现出快速的运动反应。这种反应可以从排列在多孔板中的幼虫中同时进行跟踪。由此产生的数据为神经行为学提供了新的见解。然而，这些数据的特征对传统的统计检验提出了挑战。例如，许多幼虫几乎没有或没有运动。因此，幼虫的反应有很多零值且不平衡。这些反应也是从同一个孔中重复测量的，导致了观测值的相关性。本研究通过广义线性混合模型（GLMM）解决了这些分析问题。该方法处理二项式响应，并描述同一组中观测值的相关性。它用于分析之前报道的数据集。在应用 GLMM 之前，将活性值转换为二项式响应（运动与不运动），以减少数据不平衡。此外，GLMM 估计了不同孔位置效应之间的变化，当比较两个生物组或条件时，这可以消除位置效应。通过解决数据不平衡和位置相关性问题，GLMM 有效地量化了斑马鱼运动反应的真正生物学效应。
