Learn, transfer, generate: Developing novel deep learning models for enhancing robustness and accuracy of small-scale single-cell RNA sequencing studies

学习、转移、生成:开发新颖的深度学习模型,以增强小规模单细胞 RNA 测序研究的稳健性和准确性

基本信息

  • 批准号:
    10535708
  • 负责人:
  • 金额:
    $ 3.76万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Project Summary. Single-cell RNA-sequencing (scRNAseq) technologies measure transcriptome-wide gene expression at the single-cell level. In contrast to bulk RNA-sequencing, scRNAseq can elucidate dynamic expression patterns between different cellular populations. A key problem in scRNAseq studies is the inability to transfer knowledge between independent sequencing studies directly. As a result, it has been necessary for researchers to spend a significant amount of time and resources generating massive datasets to enable meaningful analyses, a process that is costly and often not reproducible. Another transformative technology is spatial transcriptomics (ST), which provides genetic profiles of cells while containing the positional information on the sequenced cell. ST has the potential to expand our understanding of cellular heterogeneity, interactions, and pathology; however, ST is still an emerging technology and is not widely available for many studies. This proposal will fulfill the unmet need for scalable algorithms that transfer knowledge from existing datasets to new studies, leveraging learned representations to construct the sequenced tissue's spatial information. I propose to achieve these goals through the following aims: (1) Transfer knowledge from existing public single- cell data to new experimental data using a deep neural-attention network, and (2) develop the first spatially- informed model for generating realistic scRNAseq data. In Aim 1, I will use the "attention" mechanisms (which have revolutionized many fields in computer science) to learn complex gene dependencies intelligently and learn important biological features (e.g., marker genes) in a fully self-supervised manner, providing biological interpretability that is desperately needed. Such a model can be used in many tasks and for datasets with relatively few samples. The learned knowledge obtained from Aim 1 will be used directly in Aim 2. In Aim 2, I will build upon our state-of-the-art generative model to generate synthetic data that contains spatial information (coordinates) of sequenced cells, even when no atlas is available. This model will allow researchers to produce synthetic data with spatial information and augment sparse and noisy datasets for more robust and accurate analyses, all possible without the need for additional costly experiments. This proposal will support my dissertation research, which will be the foundational body of work for my career as a researcher in computational genomics. During the tenure of this award, I will receive specialized training in the underlying mathematics and biology needed for developing frameworks for scRNAseq analysis. I will contribute to the existing literature by developing novel methodology and creating open-source software, making our tools and models easily accessible to the broader scientific community. Achieving the proposed aims will significantly enhance scRNAseq pipelines and analysis, making them more robust and accurate. This will additionally facilitate the study of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.
项目摘要。单细胞RNA测序(scRNAseq)技术测量转录组范围的基因 在单细胞水平上表达。与批量RNA测序相比,scRNAseq可以阐明动态的 不同细胞群体之间的表达模式。scRNAseq研究的一个关键问题是, 在独立的测序研究之间直接传递知识。因此,有必要 研究人员需要花费大量的时间和资源来生成大量的数据集, 有意义的分析,这是一个昂贵的过程,往往是不可复制的。另一项变革性技术是 空间转录组学(ST),其提供细胞的遗传图谱,同时包含位置信息 在测序的细胞上ST有潜力扩大我们对细胞异质性,相互作用, 然而,ST仍然是一种新兴技术,并没有广泛用于许多研究。 该提案将满足对从现有数据集转移知识的可扩展算法的未满足需求 到新的研究,利用学到的表示来构建测序组织的空间信息。我 建议通过以下目标实现这些目标:(1)从现有的公共单一的知识转移- 细胞数据到新的实验数据,使用深度神经注意力网络,以及(2)开发第一个空间- 用于生成真实scRNAseq数据的知情模型。在目标1中,我将使用“注意力”机制( 已经彻底改变了计算机科学的许多领域)智能地学习复杂的基因依赖性, 学习重要的生物特征(例如,标记基因)以完全自我监督的方式,提供生物学 可解释性是迫切需要的。这样的模型可以用于许多任务,并用于具有 样本相对较少。从目标1中获得的知识将直接用于目标2。在目标2中,我 将建立在我们最先进的生成模型,以生成包含空间信息的合成数据 (坐标),即使当没有图谱可用时。这个模型将允许研究人员生产 具有空间信息的合成数据以及增强的稀疏和噪声数据集,以实现更强大和更准确的功能 分析,所有可能的,而不需要额外的昂贵的实验。 这个建议将支持我的论文研究,这将是我职业生涯的基础工作 作为计算基因组学的研究者。在这个奖项的任期内,我将接受专门的培训 在开发scRNAseq分析框架所需的基础数学和生物学方面。我会 通过开发新的方法和创建开源软件,为现有文献做出贡献, 使我们的工具和模型更容易为更广泛的科学界所用。实现拟议的 aims将显著增强scRNAseq管道和分析,使其更加强大和准确。这 此外,还将促进较小数据集的研究,可能减少患者和动物的数量。 在初步研究中是必要的。

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