Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states.
Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states.
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DOI：
10.1098/rsif.2015.0899
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发表时间：
2015-12-06
期刊：
影响因子：
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通讯作者：
Webb B
中科院分区：
文献类型：
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作者：
Szigeti B;Deogade A;Webb B
我们提出了一种新颖的方法，用于无监督的幼虫果蝇和秀丽隐杆线虫中的行为基序。一个基序被定义为经常复发的特定姿势序列。动物的变化姿势由特征性时间序列表示，我们在这个时间序列中寻找图案。为了找到基序，分割了特征性时间序列，并使用样条回归进行了聚类。与以前的方法不同，我们的方法可以将不等持续时间的序列分类为相同的基序。行为基序被用作概率行为注释者特征性注释者（ESA）的基础。概率注释避免了刚性阈值值，并允许量化分类不确定性。我们对幼虫果蝇和秀丽隐杆线虫都应用了特征性注释，并与行为状态的手注释良好。但是，我们发现许多行为事件不能明确分类。通过将结果与人造药物行为的ESA进行比较，我们认为歧义是由于行为状态之间的连续性比这些生物通常所假定的要大。
We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans. A motif is defined as a particular sequence of postures that recurs frequently. The animal's changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a probabilistic behavioural annotator, the eigenshape annotator (ESA). Probabilistic annotation avoids rigid threshold values and allows classification uncertainty to be quantified. We apply eigenshape annotation to both larval Drosophila and C. elegans and produce a good match to hand annotation of behavioural states. However, we find many behavioural events cannot be unambiguously classified. By comparing the results with ESA of an artificial agent's behaviour, we argue that the ambiguity is due to greater continuity between behavioural states than is generally assumed for these organisms.