1 Technology Development Studio, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
2 Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme, Dresden, Germany.
SLAS Discov. 2019 Mar;24(3):234-241. doi: 10.1177/2472555218818053. Epub 2019 Jan 7.
Phenotypic screens using automated microscopy allow comprehensive measurement of the effects of compounds on cells due to the number of markers that can be scored and the richness of the parameters that can be extracted. The high dimensionality of the data is both a rich source of information and a source of noise that might hide information. Many methods have been proposed to deal with this complex data in order to reduce the complexity and identify interesting phenotypes. Nevertheless, the majority of laboratories still only use one or two parameters in their analysis, likely due to the computational challenges of carrying out a more sophisticated analysis. Here, we present a novel method that allows discovering new, previously unknown phenotypes based on negative controls only. The method is compared with L1-norm regularization, a standard method to obtain a sparse matrix. The analytical pipeline is implemented in the open-source software KNIME, allowing the implementation of the method in many laboratories, even ones without advanced computing knowledge.
基于自动化显微镜的表型筛选可通过可评分的标记物数量和可提取的参数丰富度，对化合物对细胞的影响进行全面测量。数据的高维性既是丰富信息的来源，也是可能隐藏信息的噪声源。为了处理这种复杂的数据，已经提出了许多方法来降低复杂性并识别有趣的表型。然而，大多数实验室在分析中仍然只使用一个或两个参数，这可能是由于进行更复杂的分析存在计算方面的挑战。在这里，我们提出了一种仅基于阴性对照即可发现新的、以前未知表型的新方法。该方法与 L1-范数正则化（一种获取稀疏矩阵的标准方法）进行了比较。分析流程在开源软件 KNIME 中实现，允许许多实验室（即使是没有先进计算知识的实验室）实施该方法。
