An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
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DOI：
10.1016/j.ymeth.2016.08.018
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发表时间：
2017-01-01
期刊：
影响因子：
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通讯作者：
Filby A
中科院分区：
文献类型：
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作者：
Hennig H;Rees P;Blasi T;Kamentsky L;Hung J;Dao D;Carpenter AE;Filby A
成像流式细胞术能够实现潜在的强大的多重单细胞分析。用于成像流式细胞术的数据分析技术主要是手动和主观的。我们的机器学习工作流程在成像流式细胞术中识别表型。工作流程使用开源软件，不需要计算专业知识。成像流式细胞术（IFC）能够从数十万个单细胞中高通量收集形态和空间信息。这种内容丰富、信息丰富的图像数据在理论上可以解决复杂的、通常是异质的生物样品之间的重要生物差异。然而，数据分析通常以高度手动和主观的方式使用非常有限的图像分析技术结合常规流式细胞术门控策略来进行。这种方法不能扩展到每个细胞的数百个可用的基于图像的特征，因此只利用了一小部分的空间和形态信息。因此，结果的质量、可重复性和严谨性受到数据分析师的技能、经验和独创性的限制。在这里，我们描述了一个使用开源软件的管道，该管道使用机器学习算法利用数字图像中的丰富信息。来自成像流式细胞仪的补偿和校正的原始图像文件（.rif）数据文件（专有的.cif文件格式）被导入开源软件CellProfiler，其中图像处理管道识别细胞和亚细胞区室，允许测量数百个形态学特征。然后，可以使用先进的机器学习和聚类方法，使用“用户友好”的平台（如CellProfiler Analyst）来分析这些高维数据。研究人员可以训练自动化细胞分类器来识别不同的细胞类型、细胞周期阶段、药物治疗/控制条件等，使用监督机器学习。这一工作流程应使科学界能够充分利用国际金融公司数据集的分析能力。这将有助于基于可能对人眼隐藏的特征来揭示否则未被欣赏的细胞群，所述特征包括在无标记检测通道（例如明场和暗场图像）中的细微测量差异。
Imaging flow cytometry enables potentially powerful, multiplexed single-cell analysis. Data analysis techniques for imaging flow cytometry are largely manual and subjective. Our machine learning workflow identifies phenotypes in imaging flow cytometry. The workflow uses open-source software and does not require computational expertise. Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.