Learn, transfer, generate: Developing novel deep learning models for enhancing robustness and accuracy of small-scale single-cell RNA sequencing 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。与整体RNA测序相比，scRNAseq可以解释动态 不同细胞群体之间的表达模式。ScRNAseq研究中的一个关键问题是无法 在独立的测序研究之间直接传递知识。因此，有必要对 研究人员花费大量时间和资源生成海量数据集，以实现 有意义的分析，这是一个昂贵且往往不可重现的过程。另一项变革性的技术是 空间转录学(ST)，它提供细胞的遗传图谱，同时包含位置信息 在被测序的细胞上。ST有可能扩大我们对细胞异质性、相互作用、 和病理学；然而，ST仍然是一项新兴技术，在许多研究中并不广泛使用。 这一提议将满足对从现有数据集中转移知识的可扩展算法的未得到满足的需求 对于新的研究，利用已学习的表示法来构建已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)从现有的公共部门转移知识-- 使用深度神经注意网络将细胞数据转换为新的实验数据，以及(2)开发第一个空间-注意网络。 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”机制(它 使计算机科学的许多领域发生了革命性的变化)智能地学习复杂的基因依赖性 以完全自我监督的方式学习重要的生物学特征(例如，标记基因)，提供生物学 这是迫切需要的可解释性。这样的模型可用于许多任务以及具有 相对较少的样本。从目标1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之上，以生成包含空间信息的合成数据 已排序单元格的(坐标)，即使没有可用的图集也是如此。这种模型将使研究人员能够生产出 具有空间信息的合成数据，并增加稀疏和噪声数据集，以实现更健壮和准确 分析，所有这些都是可能的，而不需要额外的昂贵的实验。 这项建议将支持我的论文研究，这将是我职业生涯的基础性工作 作为一名计算基因组学研究人员。在这个奖项的任期内，我将接受专门的培训 在开发scRNAseq分析框架所需的基本数学和生物学方面。这就做 通过开发新的方法和创建开放源码软件来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使用。实现建议的 AIMS将显著增强scRNAseq管道和分析，使其更加健壮和准确。这 将进一步促进对较小数据集的研究，潜在地减少患者和动物的数量 在最初的研究中是必要的。

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.