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Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference. 空间转录组学为轨迹推断带来新的挑战和机遇
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-11-14 DOI: 10.1146/annurev-biodatasci-040324-030052
Matthieu Heitz, Yujia Ma, Sharvaj Kubal, Geoffrey Schiebinger

Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.

空间转录组学(ST)为单细胞数据分析带来了新的维度。虽然有些数据分析方法无需进行重大修改即可移植,但它们只是例外,而不是常规。特别是轨迹推断(TI)方法,由于 ST 数据的空间批次效应,可能会面临巨大的挑战。这可能会给每个时间点增加独立的噪声源。ST 数据轨迹推断的开创性方法主要侧重于解决物理排列中的批次效应,即组织在不同时间点以不同方式变形。然而,由于 ST 技术的测量粒度以及切片产生的偏差,也带来了其他挑战。在本综述中,我们研究了这些挑战的来源,并探讨了当前最先进的 STTI 方法如何应对这些挑战。最后,我们强调了未来方法发展的一些机遇。
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引用次数: 0
Centralized and Federated Models for the Analysis of Clinical Data. 临床数据分析的集中模式和联合模式。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-122220-115746
Ruowang Li, Joseph D Romano, Yong Chen, Jason H Moore

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

精准医学研究的进展取决于对广泛而多样的临床数据集的收集和分析。随着临床数据集的模式、规模和来源的不断扩大,当务之急是设计出从这些不同来源汇总信息的方法,以实现对疾病的全面了解。在这篇综述中,我们介绍了分析多样化临床数据集的两种重要方法,即集中模式和联合模式。我们比较和对比了每种模式固有的优缺点,并介绍了方法论的最新进展及其相关挑战。最后,我们展望了这两种模式为未来临床数据分析带来的机遇。
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引用次数: 0
The Evolutionary Interplay of Somatic and Germline Mutation Rates. 体细胞和种系突变率在进化过程中的相互作用
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-104225
Annabel C Beichman, Luke Zhu, Kelley Harris

Novel sequencing technologies are making it increasingly possible to measure the mutation rates of somatic cell lineages. Accurate germline mutation rate measurement technologies have also been available for a decade, making it possible to assess how this fundamental evolutionary parameter varies across the tree of life. Here, we review some classical theories about germline and somatic mutation rate evolution that were formulated using principles of population genetics and the biology of aging and cancer. We find that somatic mutation rate measurements, while still limited in phylogenetic diversity, seem consistent with the theory that selection to preserve the soma is proportional to life span. However, germline and somatic theories make conflicting predictions regarding which species should have the most accurate DNA repair. Resolving this conflict will require carefully measuring how mutation rates scale with time and cell division and achieving a better understanding of mutation rate pleiotropy among cell types.

新的测序技术使测量体细胞系突变率变得越来越可能。精确的种系突变率测量技术也已问世十年,这使得评估这一基本进化参数在整个生命树中的变化情况成为可能。在此,我们回顾了一些关于种系和体细胞突变率进化的经典理论,这些理论是利用群体遗传学和衰老与癌症生物学原理提出的。我们发现,体细胞突变率的测量结果虽然在系统发育多样性方面仍然有限,但似乎与保护体细胞的选择与寿命成正比的理论相一致。然而,生殖细胞理论和体细胞理论在预测哪个物种的 DNA 修复最准确方面存在冲突。要解决这一矛盾,需要仔细测量突变率如何随时间和细胞分裂而变化,并更好地了解细胞类型之间的突变率褶积性。
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引用次数: 0
Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. 绘制细胞和疾病表型的多尺度蛋白质组组织图。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-113534
Anthony Cesnik, Leah V Schaffer, Ishan Gaur, Mayank Jain, Trey Ideker, Emma Lundberg

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

虽然人类蛋白质的主要序列已经编目十多年,但确定这些蛋白质是如何组织成一个动态的多蛋白集合体,其结构和功能跨越生物尺度,仍是一项持续的工作。对这些高阶结构的系统化和数据驱动分析正在兴起,有助于发现和理解细胞表型。目前,有关蛋白质定位和功能的知识主要来自人工注释和基因本体等资源的整理,这些资源偏重于文献中注释丰富的基因。在这里,我们设想了一个由数据驱动的蛋白质组装图谱驱动的未来。通过整合跨长度尺度和时间尺度的蛋白质表达、定位和相互作用数据,这些图谱可以捕捉和解码细胞功能。在这篇综述中,我们将重点介绍构建集成细胞图谱的进展,以加速生命科学和转化研究的发展。
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引用次数: 0
Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. 在阿尔茨海默病研究中采用信息学策略:从遗传学、多组学、生物标记物到临床结果的回顾。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102423-121021
Jingxuan Bao, Brian N Lee, Junhao Wen, Mansu Kim, Shizhuo Mu, Shu Yang, Christos Davatzikos, Qi Long, Marylyn D Ritchie, Li Shen

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

阿尔茨海默氏症(AD)是一个全国性的重大问题,影响到 580 万人,每年造成的损失超过 2,500 亿美元。然而,目前尚无治疗方法。因此,迫切需要有效的策略来发现阿兹海默症生物标志物,以用于疾病的早期检测和药物开发。在这篇综述中,我们从生物医学数据科学家的角度研究了AD,讨论了AD研究的四个基本组成部分:遗传学(G)、分子多组学(M)、多模态成像生物标志物(B)和临床结果(O)(统称为GMBO框架)。我们全面回顾了 GMBO 框架中每个组成部分的常用统计和信息学方法,并附有具有里程碑意义的 AD 研究的主要发现。我们的综述强调了多模态生物库数据在应对 AD 关键挑战(如早期诊断、疾病异质性和治疗开发)方面的潜力。我们指出了 AD 研究中的主要障碍,包括数据稀缺性和复杂性,并倡导加强合作、统一数据和采用先进的建模技术。这篇综述旨在成为了解当前 AD 研究中生物医学数据科学策略的重要指南,强调我们需要综合、多学科的方法来促进我们对 AD 的理解和管理。
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引用次数: 0
Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. 空间分辨单细胞图像学:方法、挑战和未来展望》。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-103640
Felipe Segato Dezem, Wani Arjumand, Hannah DuBose, Natalia Silva Morosini, Jasmine Plummer

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

将全局组学数据叠加到空间生物维度上是一项前景广阔的技术,可提供对正常和疾病状态下组织样本分子微环境组织的相互作用组和细胞异质性的高分辨率洞察。空间全息技术可分为三种主要模式:(a) 基于新一代测序的检测,(b) 基于成像的空间分辨转录组学 RNA 方法,包括原位杂交/原位测序,以及 (c) 基于成像的蛋白质组学。这些方法可在细胞水平评估转录本和蛋白质,产生大量计算难度高的数据集。由于缺乏标准化的计算管道来分析和整合这些非统一结构的数据,因此有必要应用人工智能和机器学习策略来最好地可视化和转化其复杂性。在这篇综述中,我们总结了目前可用的技术和计算策略,强调了它们的优势和局限性,并讨论了它们在科学领域的未来前景。
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引用次数: 0
Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. 在多模态 Omics 数据整合中利用人工智能:为精准医学的下一个前沿领域铺平道路。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-103801
Yonghyun Nam, Jaesik Kim, Sang-Hyuk Jung, Jakob Woerner, Erica H Suh, Dong-Gi Lee, Manu Shivakumar, Matthew E Lee, Dokyoon Kim

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

将多组学数据与电子健康记录中的详细表型分析整合在一起,标志着生物医学研究模式的转变,为人们提供了无与伦比的健康和疾病路径的整体视角。本综述描述了多模态组学数据整合的现状,强调了其在全面了解复杂生物系统方面的变革潜力。我们探讨了强大的数据整合方法,从基于连接的方法到基于转换和基于网络的策略,旨在利用不同数据类型的复杂细微差别。我们的讨论范围从纳入大规模群体生物库到剖析单细胞水平的高维 omics 层面。这篇综述强调了大型语言模型在人工智能中的新兴作用,预计它们的影响将在不久的将来成为数据整合方法的支点。在强调成就和障碍的同时,我们主张共同努力建立复杂的整合模型,为精准医学的突破性发现奠定基础。
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引用次数: 0
Privacy-Enhancing Technologies in Biomedical Data Science. 生物医学数据科学中的隐私增强技术。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 DOI: 10.1146/annurev-biodatasci-120423-120107
Hyunghoon Cho, David Froelicher, Natnatee Dokmai, Anupama Nandi, Shuvom Sadhuka, Matthew M Hong, Bonnie Berger

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

生物医学数据储存库的规模和种类迅速增加,引起了人们对隐私问题的关注。收集和共享人体数据的传统框架对隐私的保护有限,往往需要建立数据孤岛。隐私增强技术(PET)有望在保护隐私的同时,通过提供共享和分析敏感数据的方法来保护这些数据并扩大其使用范围。在此,我们回顾了著名的 PET,并说明了它们在推动生物医学发展方面的作用。我们描述了 PET 的关键用例及其最新技术进展,并重点介绍了 PET 在一系列生物医学领域的最新应用。最后,我们讨论了在生物医学数据科学中更广泛地采用 PETs 所面临的挑战和需要解决的社会问题。
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引用次数: 0
Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication. 绘制人类细胞表面相互作用组:解码细胞间通讯的一把钥匙
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI: 10.1146/annurev-biodatasci-102523-103821
Jarrod Shilts, Gavin J Wright

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

细胞表面的蛋白质是一个细胞与另一个细胞之间的物理连接点,可实现细胞间的直接交流和内聚结构。随着生物医学研究从描述单个细胞向了解人体的多细胞组织飞跃,细胞表面分子之间的结合相互作用对于计算模型和临床利用这些有影响力的受体通路都是至关重要的。为了实现这一更远大的愿景,我们必须汇集表面蛋白连接方式的全部相互作用组。本综述探讨了我们离了解人类细胞表面蛋白相互作用组还有多远。我们总结了用于组装表面蛋白相互作用组的数据库和系统技术的现状,同时强调了仍然存在的巨大差距。我们希望以此为路线图,最终建立一个更强大的人类细胞表面蛋白相互作用组图谱。
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引用次数: 0
Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives. 医疗保健数据中的疾病轨迹:方法、主要成果和未来展望。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-01 DOI: 10.1146/annurev-biodatasci-110123-041001
Isabella Friis Jørgensen, Amalie Dahl Haue, Davide Placido, Jessica Xin Hjaltelin, Søren Brunak

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

疾病轨迹被定义为连续的、方向性的疾病关联,在全人口电子医疗数据的可用性和充足的计算能力的推动下,疾病轨迹已成为一个热门研究领域。在此,我们以欧洲的研究为重点,概述了疾病轨迹研究,包括用于构建疾病轨迹的本体论和计算方法。我们还讨论了疾病轨迹的不同应用,从描述性风险识别到疾病进展、患者分层以及使用机器学习进行个性化预测。我们描述了该领域的挑战和机遇,这些挑战和机遇最终将受益于欧洲健康数据空间(European Health Data Space)等倡议,随着时间的推移,这些倡议将使分析来自数亿患者队列的数据成为可能。
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引用次数: 0
期刊
Annual Review of Biomedical Data Science
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