Integrating multimodal cancer data using deep latent variable path modelling

IF 23.9 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alex Ing, Alvaro Andrades, Marco Raffaele Cosenza, Jan O. Korbel
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Abstract

Cancers are commonly characterized by a complex pathology encompassing genetic, microscopic and macroscopic features, which can be probed individually using imaging and omics technologies. Integrating these data to obtain a full understanding of pathology remains challenging. We introduce a method called deep latent variable path modelling, which combines the representational power of deep learning with the capacity of path modelling to identify relationships between interacting elements in a complex system. To evaluate the capabilities of deep latent variable path modelling, we initially trained a model to map dependencies between single-nucleotide variant, methylation profiles, microRNA sequencing, RNA sequencing and histological data using breast cancer data from The Cancer Genome Atlas. This method exhibited superior performance in mapping associations between data types compared with classical path modelling. We additionally performed successful applications of the model to stratify single-cell data, identify synthetic lethal interactions using CRISPR–Cas9 screens derived from cell lines and detect histologic–transcriptional associations using spatial transcriptomic data. Results from each of these data types can then be understood with reference to the same holistic model of illness.

Abstract Image

利用深潜变量路径模型整合多模态癌症数据
癌症通常具有复杂的病理特征,包括遗传,微观和宏观特征,可以使用成像和组学技术单独探测。整合这些数据以获得对病理的全面理解仍然具有挑战性。我们引入了一种称为深潜变量路径建模的方法,该方法将深度学习的表征能力与路径建模的能力相结合,以识别复杂系统中相互作用元素之间的关系。为了评估深潜变量路径建模的能力,我们首先训练了一个模型来绘制单核苷酸变异、甲基化谱、microRNA测序、RNA测序和组织学数据之间的依赖关系,使用来自癌症基因组图谱的乳腺癌数据。与经典路径模型相比,该方法在数据类型之间的映射关联方面表现出优越的性能。此外,我们还成功地应用该模型对单细胞数据进行分层,使用来自细胞系的CRISPR-Cas9筛选识别合成的致命相互作用,并使用空间转录组学数据检测组织学-转录关联。然后,可以参照相同的疾病整体模型来理解每种数据类型的结果。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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