Normalization of large-scale behavioural data collected from zebrafish
Year: 2019
Type: article
Abstract: 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.
Source: PLoS ONE
Language: English
Subfield: Cell Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,595
Funders: National Science Foundation, Howard Hughes Medical Institute, International Retinal Research Foundation, Schlumberger Foundation, Purdue University, National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Chinese University of Hong Kong, National Institutes of Health, National Institute of General Medical Sciences, National Center for Research Resources
Awards: G1148457656, Collaborative Research: ATD: Integrated statistical algorithms with ultra-high performance computing for discovering SNPs from massive next-generation metagenomic sequencing data, G2822815985, G3240518821, G4092070788, Deciphering the Regulatory Role of Small RNAs on Alternative Splicing , G5625049224, PFI-TT: Novel Silicon Photovoltaics for Efficient and Low-cost Conversion of Heat to Electricity, CAREER: Subsampling Methods in Statistical Modeling of Ultra-Large Sample Geophysics, High Performance Computing for Biomedical Research, Novel statistical tools for cell line specific epigenetic analysis, G8113847061, Collaborative Research: Leverage Subsampling for Regression and Dimension Reduction
OpenAlex ID: W2912363038