Multidimensional Quantitative Imaging Core

项目总结/摘要 多维定量成像核心将提供定量图像，基因组和统计 该中心的研究人员为显微镜和测序项目进行分析。 以前，该核心作为计算生物学核心运作，只专注于提供专业知识， P50研究项目的基因组和统计分析。我们扩大了 核心包括图像分析，因为许多P50研究者的项目将检查细胞和 伴随分化、成熟和细胞谱系变化的亚细胞形态学变化 规范.核心的目标之一是将深度学习的新进展应用于图像识别 细胞结构。由于应用深度学习技术需要大量的计算资源， 除了图像采集和分析方面的专业知识外，核心设施更适合并配备了更好的设备， 处理这些类型的分析比个人调查员或团队。The Core正在与UCSF合作， 细胞构建中心开发用于细胞图像分析的通用深度学习算法 生物学核心还将通过帮助开发成像技术，将“组学”数据与成像数据相结合。 novo映射分析，以整合不同的数据集。最后，核心成员将参加培训， 教育P50学生，实习生和研究员在生物信息学和图像分析的最佳实践。

PROJECT SUMMARY/ABSTRACT The Multidimensional Quantitative Imaging Core will provide quantitative image, genomic and statistical analysis for microscopy and sequencing projects generated by the Center's research investigators. Formerly, the Core operated as a Computational Biology Core and focused only on providing expertise for genomic and statistical analysis for the P50 investigators' projects. We have expanded the scope of the Core to include image analysis since many of the P50 investigators' projects will examine cellular and subcellular morphological changes that accompany changes in differentiation, maturation and cell lineage specification. One of the goals of the core is to apply new advances in deep learning to image recognition of cellular structures. Since applying deep learning techniques requires significant computational resources as well as expertise in image acquisition and analysis, the Core facility is better suited and equipped to handle these types of analyses than individual investigators or teams. The Core is partnering with UCSF's Center of Cellular Construction to develop general deep learning algorithms for image analysis of cell biology. The Core will also integrate “omics” data with imaging data by aiding in the development of de novo mapping analyses to integrate the disparate datasets. Finally, the Core will participate in training and educating P50 students, interns and fellows in the best practices of bioinformatics and image analysis.