近期的癌症研究發現，腫瘤細胞的異質性程度與其病程發展的過程以及後續治癒的狀況有著高度的相關性。腫瘤異質性描述的狀況是指一顆癌化腫瘤內經常存在著非常複雜的細胞結構，這些細胞群包含了由組織細胞不斷突變出來的癌細胞以及正常的免疫細胞。為了探究腫瘤細胞的結構，分離細胞的實驗技術已經逐漸被運用在癌症研究上。然而，當前的實驗技術皆需要仰賴著龐大的經費支撐以及花費可觀的時間。相對於單細胞分離實驗，傳統巨量細胞資料有著快速便宜的優勢，藉由反褶積方法來推論巨量細胞資料中的細胞成分。因此，本研究提出數個統計模型以用來分析巨量細胞資料中細胞群落的組成成分。針對不同的主題，我們提出了CloneDeMix，BayICE和PEACH三種方法。首先CloneDeMix是採用混和模型來分群在DNA拷貝數上發生變異的腫瘤細胞群。不同於CloneDeMix分析癌細胞，BayICE的目的在於推論組織中各種免疫細胞的比例，其運用分層貝氏模型同時整合基因挑選以達到資料拆解的最佳效果。最後，我們研究發現分離細胞的技術會導致基因表現的改變，因此提出一個懲罰反褶積模型，稱作PEACH，同時推論巨量細胞資料中的細胞組成以及分析因為細胞分離而改變行為的基因。所有的分法都藉由分析模擬資料以及真實資料以確認模型的估計效能，同時我們也將這些方法運用在癌症以及免疫疾病的資料上。這些方法都有對應的R程式，皆可在R軟體上操作。

Tumor tissue samples comprise a mixture of cancerous and surrounding normal cells. Inferring the cell heterogeneity of tumors is critical to the understanding of cancer prognosis and the treatment decisions. Compared with the experimental methods of using cell sorting technology to isolate cell components, in silico decomposition of mixed cell samples is faster and cheaper. The present study introduces three novel statistical approaches, CloneDeMix, BayICE, and PEACH, for different issues to perform the cellular proportion estimation as well as the genomic inference. First, CloneDeMix adopts clustering approach to dissect the tumor subclonal architecture induced by copy number aberration of genes through DNA sequencing data. Different from CloneDeMix analyzing cancerous cell populations, BayICE next estimates the components of tumor-infiltrating cells such as immune cells via a Bayesian framework with stochastic variable selection. Last, PEACH uses a penalized deconvolution model based on transcriptomic data to investigate the phenomenon that the genes of the particular cell types express inconsistently after cell sorting. These models were validated through simulated data and real data to demonstrate the performance of deconvolution. Furthermore, the analysis of cancer and immune-related diseases showed the results associated with biological interpretation. All of the works are implemented on the corresponding R packages which are publicly available to perform the deconvolution analysis.

Chapter 1 Introduction 9
Chapter 2 Literature review on deconvolution 12
2.1 Basic formula of deconvolution 12
2.2 Genomic deconvolution 13
2.3 Transcriptomic deconvolution 14
Chapter 3 Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations 16
3.1 Motivation 16
3.2 Methods 18
3.2.2 Estimating MCPs and copy number by using expectation–maximization algorithm 20
3.2.3 Determining the order of copy number variants 23
3.3 Result 23
3.3.1 Simulation 24
3.3.2 Comparison with THetA2 26
3.3.3 Preprocessing of TCGA data 28
3.3.4 Copy number distribution and clone structure 28
3.3.5 Inference of evolutionary order of mutations 32
3.3.6 Application on serial biopsies of esophageal cancer 35
3.3.7 Incorporating SNV data into CNA heterogeneity study 37
3.4 Discussion 39
Chapter 4 BayICE: A Hierarchical Bayesian deconvolution model with stochastic search variable selection 42
4.1 Motivation 42
4.2 BayICE deconvolution model 43
4.2.1 Input data and normalization 44
4.2.2 Statistical modeling 45
4.2.3 Novel gene selection via SSVS approach 46
4.2.4 Bayesian false discovery rate 48
4.2.5 Inflation factor 48
4.2.6 Markov chain Monte Carlo sampling procedure 50
4.3 Simulation study 53
4.3.1 Settings of multinomial simulator 53
4.3.2 Assessing inference of deconvolution 55
4.3.3 Evaluating the accuracy of gene detection 56
4.3.4 Robustness 57
4.4 Validation 59
4.5 Application to non-small cell lung cancer 61
4.5.1 Deconvolution of normal lung tissues 62
4.5.2 Deconvolution of tumors 63
4.6 Discussion 65
Chapter 5 Penalized deconvolution model accounting for cellular heterogeneity 68
5.1 Motivation 68
5.2 Materials and Methods 69
5.2.1 Deconvolution model 69
5.2.2 Parameter tuning 73
5.3 Results 74
5.3.1 Dataset and preprocess 74
5.3.2 In silico simulation 75
5.3.3 Validation 77
5.3.4 Application 78
5.4 Discussion 81
Chapter 6 Conclusion and future work 83
Reference 85
Appendix 90
Supplement for Chapter 3 90
1. Extended version to include SNV analysis 90
2. Supporting figure 95
Supplement for Chapter 4 97
Extended version to include SNV analysis 97
1. Simulators 97
2. Simulation results from the NB simulator and the normal simulator 97
Supplement for Chapter 5 100

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