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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Scoring Diverse Cellular Morphologies In Image-based Screens With Iterative Feedback And Machine Learning
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Scoring Diverse Cellular Morphologies In Image-based Screens With Iterative Feedback And Machine Learning

机译:利用迭代反馈和机器学习对基于图像的屏幕中的不同细胞形态进行评分

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摘要

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytologi-cal profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.
机译:首先通过鉴定具有可见表型的突变体并通过繁琐和主观的目视检查对屏幕中的每个样品评分,从而发现了许多生物途径。现在,自动图像分析可以有效地对许多表型评分。在实际应用中,定制图像分析算法或找到足够数量的示例细胞来训练机器学习算法可能是不可行的,尤其是在没有阳性对照样品且目标表型很少的情况下。在这里,我们提出一种有监督的机器学习方法,该方法使用迭代反馈在高通量,基于图像的屏幕中轻松对多种细微和复杂的形态表型进行评分。首先,自动细胞学分析为每个图像中的每个细胞提取数百个数字描述符。接下来,研究人员生成一个规则(即分类器),以使用迭代反馈在短暂的交互式训练中识别具有感兴趣表型的细胞。最后,将实验中的所有细胞自动分类,并根据显示表型的细胞的存在对每个样品进行评分。通过使用这种方法,我们成功地在2种生物的RNA干扰筛选中对15种不同细胞形态的流行度进行了评分,其中某些形态以前很难处理。

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  • 作者单位

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142 Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139;

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142 Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;

    Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;

    Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;

    Dana-Farber Cancer Institute and Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115;

    Dana-Farber Cancer Institute and Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115;

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142;

    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139;

    The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142 Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142 Department of Biology, Massachusetts Institute of Technology, 31 Ames Street, Cambridge, MA 02139;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    high-content screening; high-throughput image analysis; phenotype;

    机译:高含量筛选高通量图像分析表型;

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