Domain adaptation approaches to unify established and emerging sequencing technologies

项目摘要/摘要 测序技术的进步为从多个物种中询问生物系统提供了新的机会 透视。然而，新技术的引入突显了许多研究人员面临的一个问题： 缺少数据。跨技术和生物状态的遗漏观测是一个经常被观察到的问题 在计算生物学领域。这种缺失可能是由于技术上的限制，即稀有性 生物状态，或者是因为这项技术没有被广泛采用。虽然有一种技术可能具有 生物观测中的高度稀疏性，就有机会利用现有的、来自 一种成熟的技术来计算丢失的生物观测数据。 我们通过利用机器学习中的新方法进步来解决这些问题，主要集中在 领域适配技术。这些技术学习一个数据集中可以适应另一个数据集中的模式 数据集，实现跨技术的信息共享。我们的提案引入了一个总体框架，在该框架中 领域适应技术可以用来将一种新兴技术与另一种不同的技术结合起来。至 为了突出这种方法的广泛实用性，我们将该模型应用于三个生物医学应用：1)预测 类风湿关节炎的细胞类型特异性扰动反应；2)从无细胞DNA预测组织起源 (CfDNA)；3)从急性髓系白血病(AML)的无细胞DNA中预测祖细胞特异性基因特征。 拟议的目标不仅是统一现有的和新兴的测序技术，而且能够发现 难以或不可能直接观察到的新生物学。 提出的研究建立在我使用转录数据统计方法的经验基础上。在.期间 K99阶段I将需要我的指导团队在深度生成性建模方面进行进一步培训(Dr.Casey 单细胞数据的建模(张帆博士)以及cfDNA和染色质可及性的建模(Dr 斯里尼瓦斯·拉马钱德兰)。这项研究将在科罗拉多大学安舒茨医学分校进行 校园里，在健康人工智能中心。在这个机构里，我将有权进入科罗拉多州临床和 翻译科学研究所和RNA生物科学倡议，它们为建立 跨学科和转化性研究计划。有了这次培训和可用的机构资源，我 将有坚实的基础，在此基础上建立一个专注于领域适应的独立研究计划 高通量测序技术的应用。

PROJECT SUMMARY / ABSTRACT Advances in sequencing technologies provide new opportunities to interrogate biological systems from multiple perspectives. However, the introduction of new technologies highlights a problem many researchers face: missing data. Missing observations across technologies and biological states is a frequently observed problem in the field of computational biology. This missingness can be a result of limitations in the technology, the rarity of a biological state, or because the technology has not been widely adopted. While one technology may have high sparsity in biological observations, there is an opportunity to leverage existing, complementary data from an established technology to impute the missing biological observations. We address these issues by utilizing new methodological advances in machine learning, primarily focusing on domain adaptation techniques. These techniques learn patterns in one dataset that can be adapted to another dataset, enabling cross-technology information sharing. Our proposal introduces a general framework in which domain adaptation techniques can be used to unite an emerging technology with a different, but technology. To highlight the broad utility of this approach, we apply this model to three biomedical applications: 1) Predict cell-type-specific perturbation response in rheumatoid arthritis; 2) Predict tissue-of-origin from cell-free DNA (cfDNA); 3) Predict progenitor-specific gene signatures from cell-free DNA in acute myeloid leukemia (AML). The proposed aims not only unite existing and emerging sequencing technologies, but enable the discovery of new biology that is difficult or infeasible to directly observe. The research proposed builds on my experience in using statistical approaches for transcriptomic data. During the K99 phase I will require further training from my mentoring team in deep generative modeling (Dr. Casey Greene), modeling of single-cell data (Dr. Fan Zhang), and modeling of cfDNA and chromatin accessibility (Dr. Srinivas Ramachandran). The research will be conducted at the University of Colorado, Anschutz Medical Campus, in the Center for Health AI. In this institution, I will have access to the Colorado Clinical and Translational Sciences Institute and the RNA Bioscience Initiative, which provide resources for building an interdisciplinary and translational research program. With this training and available institutional resources, I will have a solid foundation on which to build an independent research program focused on domain adaptation applications for high-throughput sequencing technologies.