论文标题
通过延时摄影的特征工程量化酵母菌菌落形态
Quantifying yeast colony morphologies with feature engineering from time-lapse photography
论文作者
论文摘要
贝克的酵母（酿酒酵母）是研究在多细胞菌落规模的形态的模型生物。为了了解形态的发展方式，我们收集了一张静脉内菌株生长的延时照片数据集。我们讨论使用延时照片提取时间依赖性特征时会出现的一般统计挑战。特别是，我们展示了如何成功应用基于纹理的功能工程和代表性聚类，以使用我们的数据集对酵母菌形态的开发进行分类。图像处理中的局部二进制图案（LBP）用于评分菌落的表面纹理。这种质地得分在增长过程中沿平滑轨迹发展。所采用的路径取决于形态的出现。菌落的分层聚类是根据其纹理发展轨迹进行的。聚类方法是为实用的解释性而设计的。它获得了任何分层亚群集的最佳代表性菌落图像。
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.
