Statistical Methods for RNA-seq Data Analysis

项目总结/摘要 单细胞RNA-seq（scRNA-seq）数据彻底改变了我们对细胞水平生物学的理解。空间 转录组学通过提供基因表达的空间背景进一步推动了该领域的发展。这些令人兴奋 技术已经应用于许多基础科学或临床研究项目，以了解生命系统 或疾病诊断、治疗和预防的生物学基础。通过scRNA-seq或 空间转录组学通常具有高维度（基因的数量）和大样本量（基因的数量）。 细胞或空间点）。许多生物过程的基础上观察到的基因表达数据可能是 高维基因表达数据的非线性函数。大样本量结合非线性 高维数据的信号使得深度学习成为分析scRNA-seq或空间的合适工具。 转录组学数据。早期scRNA-seq或空间转录组学的深度学习工作集中在非 监督任务，如去噪或聚类。对于许多生物医学应用，下一步自然是 监督分析，例如，在两种条件下比较scRNA-seq或空间转录组学。有 在这个方向上，深度学习方法面临两个一般挑战：可解释性 和单个细胞的噪声标签。在这个项目中，我们的目标是通过一个灵活的 将基因注释纳入深度学习的方法。为了处理带有噪声标签的单细胞，我们 提出一种迭代地细化细胞标签的混合模型和预测细胞标签的神经网络。我们 空间转录组学的工作重点是使用这些数据来训练深度学习模型， 组织学图像，特别是H&E染色的组织学图像。我们的方法提供了空间注释 在细胞类型比例和任何两种细胞类型之间的相互作用方面的组织学图像。组织学 图像在许多临床环境中普遍可用。相比之下，空间转录组学更难扩展 由于成本和物流方面的挑战。我们的方法能够从空间转录组学转移知识， 到组织学图像。一旦通过适当的训练数据集进行训练， 组织学图像，我们的方法可以应用于分析数据集只有组织学图像和评估 它们与表型或临床结果的关系。总之，我们的计算方法解决了 scRNA-seq或空间转录组学数据分析的基本问题，它们适用于大多数 产生相关数据的基础科学或临床研究项目。

Project Summary/Abstract Single cell RNA-seq (scRNA-seq) data have revolutionized our understanding of biology at cell level. Spatial transcriptomics further moves the field forward by providing spatial context of gene expression. These exciting techniques have been applied in many basic science or clinical research projects to understand living systems or the biological basis for disease diagnosis, treatment, and prevention. The data generated by scRNA-seq or spatial transcriptomics typically has high dimension (the number of genes) and large sample size (the number of cells or spatial spots). Many biological processes underlying the observed gene expression data are likely non-linear functions of high dimensional gene expression data. Large sample size combined with non-linear signals of high dimensional data makes deep learning an appropriate tool to analyze scRNA-seq or spatial transcriptomics data. Earlier deep learning works on scRNA-seq or spatial transcriptomics focus on un- supervised tasks, such as de-noising or clustering. For many biomedical applications, a natural next step is supervised analysis, e.g., comparing scRNA-seq or spatial transcriptomics between two conditions. There are much fewer works in this direction where deep learning methods face two general challenges: interpretability and noisy labels of single cells. In this project, we aim to address the interpretability challenge by a flexible method to incorporate gene annotation into deep learning. To work with single cells with noisy labels, we propose a mixture model that iteratively refines cell labels and the neural network that predicts cell labels. Our work on spatial transcriptomics focuses on using these data to train deep learning models to interpret histological images, particularly H&E stained histological images. Our method provides spatial annotation of histological images in terms of cell type proportions and interactions between any two cell types. Histological images are universally available in many clinical settings. In contrast, spatial transcriptomics is harder to scale due to cost and logistic challenges. Our method enables the transfer of knowledge from spatial transcriptomics to histological images. Once trained by an appropriate training dataset with both spatial transcriptomics and histological images, our method can be applied to analyze datasets with only histological images and assess their associations with phenotypic or clinical outcomes. In summary, our computation methods address fundamental questions on scRNA-seq or spatial transcriptomics data analysis and they are applicable for most basic science or clinical research projects that produce relevant data.