在Cellprofiler中使用机器学习进行高通量成像实验的质量控制
Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.
作者信息
Bray Mark-Anthony, Carpenter Anne E
机构信息
Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
Imaging Platform, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA.
出版信息
Methods Mol Biol. 2018;1683:89-112. doi: 10.1007/978-1-4939-7357-6_7.
Abstract
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can cause artifacts such as focus blur and image saturation, contaminating downstream analysis and impairing identification of subtle phenotypes. Here, we describe an automated quality control protocol implemented in validated open-source software, leveraging the suite of image-based measurements generated by CellProfiler and the machine-learning functionality of CellProfiler Analyst.
摘要
自动化显微镜和定量图像分析实现了对视觉细胞表型的强大高内涵筛选。识别和去除常见的基于图像的像差对于筛选工作流程至关重要。失焦图像、碎片和自发荧光样本会导致诸如焦点模糊和图像饱和等伪像，污染下游分析并妨碍对细微表型的识别。在这里，我们描述了一种在经过验证的开源软件中实施的自动化质量控制方案，利用CellProfiler生成的一系列基于图像的测量值以及CellProfiler Analyst的机器学习功能。
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