深度评估microRNA差异表达分析工具和基于DNA甲基化数据的免疫细胞量化算法

发布时间：2021-06-08 22:49

　　本研究对两方面的计算工具进行了全面评估,包括1)从小RNA测序数据中检测micro RNA（miRNA）的差异表达;2)利用组织来源的DNA甲基化（DNAm）数据估计免疫细胞比例。第一章,我们对miRNA进行了总体回顾。重点介绍了miRNA的生物生成、miRNA异构体的分类、miRNA的靶向预测以及miRNA在癌症中的作用。第二章,我们重点回顾了基于DNAm数据的去卷积方法。简要讨论了EWAS、DNAm数据库、基于DNAm的去卷积分析和免疫反应对癌症的潜在意义。第三章,miRNA异构体（isomiRs）是由与原型miRNA同臂产生的,在5和/或3个末端有几个核苷酸不同的miRNA。这些保守较高的isomiRs有着重要的生物学功能,并且对于miRNA的进化也十分重要。准确检测miRNA的差异表达,可以为miRNA的细胞功能带来新的见解,并进一步改善基于miRNA的诊断和预后应用。然而,在miRNA差异表达分析中,很少有方法考虑到isomiRs的差异表达。为了克服这一挑战,我们利用同一miRNAs的isomiRs数据的多维结构,开发了一种新颖的基于Hotelling’s T²

【文章来源】：浙江大学浙江省 211工程院校 985工程院校 教育部直属院校

【文章页数】：129 页

【学位级别】：博士

【文章目录】：
Acknowledgements
中文摘要
Abstract
Abbreviations
Chapter 1:Literature review of micro RNA in human disease
    1.1 Introduction
    1.2 MicroRNA(miRNA)
    1.3 Biogenesis of canonical miRNA
    1.4 MiRNA isoforms(isomiRs)
    1.5 The roles of miRNAs in cancer
    1.6 Nomenclature of miRNA and its isoforms
    1.7 Target prediction tools and software of miRNA
    1.8 Contribution of RNA-binding proteins and noncoding RNAs in the biogenesis
    1.9 Conclusion
Chapter 2:Literature review of immune cell subtype deconvolution methods
    2.1 Introduction
    2.2 Source and platform of DNAm Data
    2.3 Deconvolution Algorithms based on DNAm
    2.4 Applications of deconvolution algorithms
    2.5 The immune response of DNAm to disease
    2.6 Conclusion
Chapter 3:MDEHT:a Multivariate Approach for Detecting Differential Expression of Micro RNA Isoforms in RNA Sequencing Studies
    3.1 Introduction
    3.2 Methods and Materials
        3.2.1 Hotelling's T~2 statistic
        3.2.2 Identification of isomiRs from miRNA-seq
        3.2.3 Simulation studies
        3.2.4 Real data analysis
        3.2.5 Cell culture
        3.2.6 Tissue specimens
        3.2.7 Oligonucleotide transfection
        3.2.8 Quantitative real-time PCR(qRT-PCR)analysis
        3.2.9 Western blot analysis
        3.2.10 Cell proliferation assay
        3.2.11 In vitro migration and invasion assays
        3.2.12 Cell cycle analysis
    3.3 Results
        3.3.1 Evaluation of Type I error rate from DEmiRs
        3.3.2 Evaluation of Jaccard similarity measurement
        3.3.3 Identification of DEmiRs in real data datasets
        3.3.4 Functional enrichment analysis of novel DEmiRs
        3.3.5 Experimental validation of a novel DEmiR
    3.4 Discussion
    3.5 Conclusion
Chapter 4:Comprehensive evaluations of computational tools for immune cell deconvolution using bulk DNA methylation data
    4.1 Introduction
    4.2 Methods and Materials
        4.2.1 Intra-sample heterogeneity deconvolution methods
        4.2.2 Simulation studies
        4.2.3 Real data analysis
        4.2.4 Construction of DNAm reference data matrix
        4.2.5 Selection of LM22 genes signature
        4.2.6 Gene expression data
        4.2.7 Survival analysis
    4.3 Results
        4.3.1 Cluster analysis of reference dataset
        4.3.2 Evaluation of different methods and signature datasets
        4.3.3 Deconvolution of immune cell fractions from DNAm data
        4.3.4 Deconvolution of immune cell fractions from gene-expression data
        4.3.5 Survival analyses
        4.3.6 Integrated analysis of cell-type decomposition from DNAm and gene expression data
    4.4 Discussion
    4.5 Conclusion
References
Appendix
    Appendix A:Tables
    Appendix B:Figures
Resume and Publications



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