摘要翻译：
共定位分析旨在通过光学成像技术研究生物分子之间复杂的空间关联。然而，现有的共定位分析工作流只评估某一感兴趣区域内的平均共定位程度，而忽略了显微技术提供的独特而有价值的空间信息。在当前的工作中，我们引入了一个新的共域化分析框架，允许我们量化每个单独位置的共域化水平，并自动识别发生共域化的像素或区域。该框架被称为空间自适应共局域化分析(SACA)，集成了用于共局域化量化的像素级局部核模型和用于利用空间信息以空间自适应方式检测共局域化的多尺度自适应传播分离策略。在模拟和真实生物数据集上的应用证明了SACA的实际优点，我们希望成为一种易于应用和鲁棒的共域分析方法。此外，还研究了SACA的理论性质，以提供严格的统计依据。
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英文标题：
《Spatially Adaptive Colocalization Analysis in Dual-Color Fluorescence
  Microscopy》
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作者：
Shulei Wang, Ellen T. Arena, Jordan T. Becker, William M. Bement,
  Nathan M. Sherer, Kevin W. Eliceiri, Ming Yuan
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最新提交年份：
2019
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
  Colocalization analysis aims to study complex spatial associations between bio-molecules via optical imaging techniques. However, existing colocalization analysis workflows only assess an average degree of colocalization within a certain region of interest and ignore the unique and valuable spatial information offered by microscopy. In the current work, we introduce a new framework for colocalization analysis that allows us to quantify colocalization levels at each individual location and automatically identify pixels or regions where colocalization occurs. The framework, referred to as spatially adaptive colocalization analysis (SACA), integrates a pixel-wise local kernel model for colocalization quantification and a multi-scale adaptive propagation-separation strategy for utilizing spatial information to detect colocalization in a spatially adaptive fashion. Applications to simulated and real biological datasets demonstrate the practical merits of SACA in what we hope to be an easily applicable and robust colocalization analysis method. In addition, theoretical properties of SACA are investigated to provide rigorous statistical justification.
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PDF链接：
https://arxiv.org/pdf/1711.00069