Department of Statistics, University of Georgia, Athens, Georgia, United States of America.
Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.
PLoS One. 2019 Feb 15;14(2):e0212234. doi: 10.1371/journal.pone.0212234. eCollection 2019.
Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour.
许多当代神经科学实验采用高通量方法同时从许多动物身上收集行为数据。由此产生的数据结构通常很复杂，并且受到系统偏差的影响，这需要新的分析和归一化方法。本研究通过建立基于线性回归建模的方法来解决归一化问题。该模型是使用从多个野生型 (WT) 斑马鱼品系在多个发育阶段获得的视觉运动反应 (VMR) 数据集建立的。VMR 是由剧烈的光变化触发的运动反应，通常从排列在 96 孔板中的多个幼虫中重复多次测量。该测定受到几种系统变化的影响。例如，机器发出的光从一个孔到另一个孔略有不同。除了光强变化外，生物复制也产生了批间变化。这些系统变化可能导致 VMR 的差异，因此必须进行归一化。我们的归一化方法明确地对这些系统变化对 VMR 的影响进行建模。它还将不同条件的活性谱归一化为共同的基线。我们的方法具有通用性，因为它可以将不同的归一化需求作为单独的因素纳入其中。这种通用性通过对三个因素的综合归一化得到了证明：光强变化、批间变化和基线。归一化后，从数据中揭示了新的生物学见解。例如，我们发现受精后 6 天 (dpf) 的 TL 品系幼虫对光起始的反应比 9-dpf 幼虫强得多，而没有归一化的先前分析表明它们的反应相对可比。通过去除系统变化，我们基于模型的归一化可以促进下游统计比较，并有助于在神经行为的高通量研究中检测真正的生物学差异。
