本研究對(duì)兩方面的計(jì)算工具進(jìn)行了全面評(píng)估,包括1)從小RNA測(cè)序數(shù)據(jù)中檢測(cè)micro RNA（miRNA）的差異表達(dá);2)利用組織來(lái)源的DNA甲基化（DNAm）數(shù)據(jù)估計(jì)免疫細(xì)胞比例。第一章,我們對(duì)miRNA進(jìn)行了總體回顧。重點(diǎn)介紹了miRNA的生物生成、miRNA異構(gòu)體的分類(lèi)、miRNA的靶向預(yù)測(cè)以及miRNA在癌癥中的作用。第二章,我們重點(diǎn)回顧了基于DNAm數(shù)據(jù)的去卷積方法。簡(jiǎn)要討論了EWAS、DNAm數(shù)據(jù)庫(kù)、基于DNAm的去卷積分析和免疫反應(yīng)對(duì)癌癥的潛在意義。第三章,miRNA異構(gòu)體（isomiRs）是由與原型miRNA同臂產(chǎn)生的,在5和/或3個(gè)末端有幾個(gè)核苷酸不同的miRNA。這些保守較高的isomiRs有著重要的生物學(xué)功能,并且對(duì)于miRNA的進(jìn)化也十分重要。準(zhǔn)確檢測(cè)miRNA的差異表達(dá),可以為miRNA的細(xì)胞功能帶來(lái)新的見(jiàn)解,并進(jìn)一步改善基于miRNA的診斷和預(yù)后應(yīng)用。然而,在miRNA差異表達(dá)分析中,很少有方法考慮到isomiRs的差異表達(dá)。為了克服這一挑戰(zhàn),我們利用同一miRNAs的isomiRs數(shù)據(jù)的多維結(jié)構(gòu),開(kāi)發(fā)了一種新穎的基于Hotelling’s T²

【文章來(lái)源】：浙江大學(xué)浙江省 211工程院校 985工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：129 頁(yè)

【學(xué)位級(jí)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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