Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape

项目摘要/摘要 侯文彬博士是一名应用数学家和计算生物学家，担任 约翰霍普金斯大学彭博公共卫生学院生物统计学博士后。她的长期工作 职业目标是通过发展计算和统计来改善疾病治疗的临床实践 利用多组学数据破译空间和时间基因调控程序的方法和实现 这些方法分为发育过程和疾病。她提出的题为《计算性》的研究 单细胞DNA甲基化的推断方法及其空间景观与先进空间的结合 转录组技术和计算方法来推断空间DNA甲基化情况，这 能够准确地评估表观基因组的空间变异性和表观基因组靶点在治疗 疾病。侯博士在香港大学完成了数学博士学位，在那里她专注于 推断和控制基因调控网络(GRN)。她的fi第一次博士后研究(2017-2019年)。 约翰霍普金斯大学的阿拉文达·查克拉瓦蒂和苏奇·萨里亚代表了fi第一次关注的焦点转移 GRN从理论基因组学到计算基因组学。她的第二个博士后研究(2019年至今)。 JHSPH的季洪凯和斯蒂芬妮·希克斯进一步提供了重建和 使用单细胞数据预测空间转录和表观基因组景观。侯博士的指导团队 由洪凯(主要导师)，博士，开发计算和统计工具方面的专家 分析单细胞基因组数据，包括重建和预测时间和空间转录 和表观经济学景观；斯蒂芬妮·希克斯(共同导师)，博士，开发统计方法方面的专家 研究单细胞中的技术可变性和空间转录；安德鲁·范伯格(共同导师)，博士， fi癌症表观遗传学的创始人之一，领导着fi第一个由美国国立卫生研究院资助的表观基因组中心。这 提供机会解决统计学、表观基因组学和空间学交叉领域的SignifiCan挑战 转录学与先进的实验技术。她的科学fic顾问是Kasper Hansen博士， 格雷戈里·海格和王晓斌，他们在计算表观基因组学、深度学习、 临床翻译和疾病预防。利用智力、实验和计算 来自所有导师和顾问的资源，以及通过JHSPH、JHU和Johns Hopkins Medicine，侯博士 将接受强化培训、指导和职业发展，以实现本课程中提出的目标 并在获奖期间取得丰硕成果。目标一号将开发预测DNA的方法 使用批量基因表达的甲基化景观。Aim 2将开发预测单细胞DNA的方法 甲基化和差异DNA甲基化。目标3将重建组织空间DNA甲基化景观 单细胞级别，在空间环境中生成评估数据集，并执行跨研究评估 使用来自DNA元素百科全书(ENCODE)、人类细胞图谱和Recount2的数据。

Project Summary/Abstract The candidate, Wenpin Hou, PhD, is an applied mathematician and computational biologist serving as a postdoctoral fellow of Biostatistics at Johns Hopkins Bloomberg School of Public Health (JHSPH). Her long-term career goal is to improve clinical practice in therapy for diseases by developing computational and statistical methods to decipher spatial and temporal gene regulatory programs using multi-omics data and implementing these methods into developmental process and diseases. The research she proposes entitled Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape combines advanced spatial transcriptomics techniques with computational methods to infer spatial DNA methylation landscape, which enables the accurate evaluation of the epigenomic spatial variability and epigenomic targets in therapy for diseases. Dr. Hou completed her PhD in Mathematics at The University of Hong Kong where she focused on inferring and controlling gene regulatory networks (GRNs). Her first postdoctoral research (2017-2019) with Drs. Aravinda Chakravarti and Suchi Saria at Johns Hopkins University (JHU) represented the first shift in focus of GRNs from theoretical to computational genomics. Her second postdoctoral research (2019-present) with Drs. Hongkai Ji and Stephanie Hicks at JHSPH has further provided complementary training in reconstructing and predicting spatial transcriptomic and epigenomic landscape using single-cell data. Dr. Hou's mentoring team consists of Hongkai Ji (primary mentor), PhD, an expert in developing computational and statistical tools for analyzing single-cell genomic data, including reconstructing and predicting temporal and spatial transcriptomic and epigenomic landscape; Stephanie Hicks (co-mentor), PhD, an expert in developing statistical methods to address technical variability and spatial transcriptomics in single cells; and Andrew Feinberg (co-mentor), PhD, one of the founders of the field of cancer epigenetics who directs the first NIH funded Epigenome Center. This offers the opportunity to tackle significant challenges in the intersection of statistics, epigenomics and spatial transcriptomics with advanced experimental techniques. Her scientific advisors are Drs. Kasper Hansen, Gregory Hager and Xiaobin Wang who have leading expertise in computational epigenomics, deep learning, clinical translation and disease prevention, respectively. Leveraging the intellectual, experimental, and computing resources from all mentors and advisors as well as through JHSPH, JHU and Johns Hopkins Medicine, Dr. Hou will receive intensive training, mentoring and career development to achieve the goals proposed in this application and have productive outcomes during the award period. Aim 1 will develop methods to predict DNA methylation landscape using bulk gene expression. Aim 2 will develop methods to predict single-cell DNA methylation and differential DNA methylation. Aim 3 will reconstruct tissue-spatial DNA methylation landscape at the single-cell level, generate evaluation datasets in spatial context, and perform across-study assessments using data from The Encyclopedia of DNA Elements (ENCODE), Human Cell Atlas, and Recount2.