Horvath Peter, Wild Thomas, Kutay Ulrike, Csucs Gabor
Light Microscopy Centre, ETH Zurich, Zurich, Switzerland.
J Biomol Screen. 2011 Oct;16(9):1059-67. doi: 10.1177/1087057111414878. Epub 2011 Aug 1.
Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning-based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.
基于成像的高内涵筛选通常依赖于对大量微观图像数据集中的细胞表型进行单细胞评估。传统上，这些筛选通过提取一些与图像相关的参数并使用它们的比率（线性单参数或多参数分离）将细胞分类到不同的表型类别中。在本研究中，作者展示了基于机器学习的单个细胞分类如何优于那些传统的基于比率的技术。利用荧光强度以及形态和纹理特征，他们评估了数据分析的性能如何随着特征数量的增加而提高。他们的发现基于一个案例研究，该研究涉及通过小干扰RNA（siRNA）筛选监测荧光标记报告蛋白在核质和核仁中的积累。为了进行分析，他们开发了一个完整的分析工作流程，包括图像分割、特征提取、细胞分类、命中检测以及结果可视化。对于分类任务，作者建立了一个新的图形框架——高级细胞分类器，它以最少的用户交互提供了非常准确的高内涵筛选分析，可使用多种先进的机器学习方法。
