Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

中文翻译：

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Facetto：将无监督学习与有监督学习相结合，在多通道图像数据中进行分层表型分析。

Facetto是一种可扩展的可视化分析应用程序，用于发现人类肿瘤和组织的高维多通道显微图像中的单细胞表型。这些图像代表了数字组织学的最前沿，并有望彻底改变研究，诊断和治疗癌症等疾病的方式。高度多路复用的组织图像非常复杂，包含109个或更多像素，60多个通道以及数百万个单个细胞。这使得手动分析具有挑战性并且容易出错。现有的自动化方法在很大程度上也是不足的，因为它们无法有效地利用解剖病理学家可获得的人体组织生物学的深厚知识。为了克服这些挑战，Facetto可以对细胞类型和状态进行半自动化分析。它将无监督和有监督的学习集成到图像和特征探索过程中，并提供了用于分析来源的工具。专家可以对数据进行聚类，以发现新型的癌症和免疫细胞，并使用聚类结果来训练卷积神经网络，从而对新细胞进行分类。同样，分类器的输出可以聚类以发现聚合模式和表型子集。我们还引入了一种新的分层方法来跟踪用户创建的分析步骤和数据子集。这有助于识别细胞类型。用户可以构建表型树，并与由此产生的高维特征和图像空间的层次结构进行交互。我们报告领域科学家探索各种大规模荧光成像数据集的用例。