Introduction

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.

Publications

  1. Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results.
    Cite this
    Horvath P, Wild T, Kutay U, Csucs G, 2011-10-01 - Journal of biomolecular screening

Credits

  1. Peter Horvath
    Developer

  2. Thomas Wild
    Developer

  3. Ulrike Kutay
    Developer

  4. Gabor Csucs
    Investigator

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Summary
AccessionBT000338
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByGabor Csucs