利用延时摄影的特征工程定量酵母菌落形态
Quantifying yeast colony morphologies with feature engineering from time-lapse photography
摘要：
面包酵母（酿酒酵母）是研究多细胞菌落规模出现的形态学的模式生物。为了了解形态学是如何发展的，我们收集了一组不同菌株的生长的延时照片。啤酒。我们讨论了使用延时照片提取时间依赖性特征时出现的一般统计挑战。特别是，我们展示了如何基于纹理的特征工程和代表性聚类可以成功地应用于分类酵母菌落形态的发展，使用我们的数据集。利用图像处理中的局部二值模式（LBP）对菌落表面纹理进行评分。这种纹理评分在生长过程中沿着平滑的轨迹发展。所采取的路径取决于形态如何出现。一个层次聚类的菌落进行根据其纹理发展轨迹。聚类方法是为实际的可解释性而设计的;它为任何层次子聚类获得最具代表性的菌落图像。
英文摘要：
Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The local binary pattern (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability; it obtains the best representative colony image for any hierarchical sub-cluster.
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