本研究對兩方面的計算工具進行了全面評估,包括1)從小RNA測序數據中檢測micro RNA（miRNA）的差異表達;2)利用組織來源的DNA甲基化（DNAm）數據估計免疫細胞比例。第一章,我們對miRNA進行了總體回顧。重點介紹了miRNA的生物生成、miRNA異構體的分類、miRNA的靶向預測以及miRNA在癌癥中的作用。第二章,我們重點回顧了基于DNAm數據的去卷積方法。簡要討論了EWAS、DNAm數據庫、基于DNAm的去卷積分析和免疫反應對癌癥的潛在意義。第三章,miRNA異構體（isomiRs）是由與原型miRNA同臂產生的,在5和/或3個末端有幾個核苷酸不同的miRNA。這些保守較高的isomiRs有著重要的生物學功能,并且對于miRNA的進化也十分重要。準確檢測miRNA的差異表達,可以為miRNA的細胞功能帶來新的見解,并進一步改善基于miRNA的診斷和預后應用。然而,在miRNA差異表達分析中,很少有方法考慮到isomiRs的差異表達。為了克服這一挑戰(zhàn),我們利用同一miRNAs的isomiRs數據的多維結構,開發(fā)了一種新穎的基于Hotelling’s T²

【文章來源】：浙江大學浙江省 211工程院校 985工程院校 教育部直屬院校

【文章頁數】：129 頁

【學位級別】：博士

【文章目錄】：
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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