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研究生:汪惠敏
研究生(外文):Hui-Min Wang
論文名稱:利用非負矩陣分解法與調整後的芮氏指標建立複雜性疾病的內在生物表現型
論文名稱(外文):Constructing Endophenotypes of Complex Diseases Using Non-Negative Matrix Factorization and Adjusted Rand Index
指導教授:范盛娟范盛娟引用關係
指導教授(外文):Cathy S.J. Fann
學位類別:博士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2012
畢業學年度:101
語文別:英文
論文頁數:93
中文關鍵詞:複雜性疾病內在生物表現型非負矩陣分解法芮氏指標
外文關鍵詞:complex diseaseendophenotypesnon-negative matrix factorizationadjusted rand indexmolecular subtypes
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複雜性疾病往往受到基因與基因,或基因與環境因素等多種因素共同影響,並非由單一基因就能導致疾病的發生。再者,若病人間存在著分子差異(molecular dissimilarity)而產生疾病亞型(disease subtypes),這些因素都會更增加複雜性疾病在基因定位上偵測的困難度與複雜度。為了簡化偵測基因定位的複雜度,內在生物表現型(endophenotype)的概念衍生而出。在生物學上,內在生物表現型和疾病有著相同的遺傳路徑,但內在生物表現型卻比診斷上的表現型(phenotype)更為接近其相關的基因且少了其他基因的交互作用與環境因素影響。這也顯示內在生物表現型在複雜性疾病基因定位的重要性。
此研究提出內在生物表現型定位方法,利用非負矩陣分解法尋找疾病的亞型,並且結合調整後的芮氏指標挑選代表每一組疾病亞型的基因,進而利用路徑分析(pathway analysis)了解基因與病理相關的機轉。為了驗證此方法在定位內在生物表現型的效能,模擬研究中利用主成分分析(principal component analysis, PCA)和K平均群集法(K-means clustering)與非負矩陣分解法進行比較。模擬結果證實非負矩陣分解法對於定位疾病亞型的正確性優於主成分分析法與K平均群集法。此外,將此方法應用於晚發型阿茲海默症(late-onset Alzheimer’s disease),此方法也成功定位出三組疾病亞型。利用這三組疾病亞型與正常人(normal subjects)進行基因表現相異度分析時,此方法也找到比疾病未分組時更多的候選易感型基因(candidate susceptibility genes)。因此,此研究分析結果有助於解離複雜性疾病的病徵,進而有效定位疾病。

Complex diseases are typically caused by combinations of molecular disturbances that might vary strongly in different patients. Endophenotypes offer a simplified approach to this problem by reducing genetic heterogeneity. To construct endophenotype of complex disease, a supervised learning method, support vector machine (SVM), was firstly tried to apply. However, there were several difficulties. For example, high-dimension data structure and missing value of covariates in control dataset. These problems were reduced the accuracy of prediction in SVM.
Since molecular dissimilarities often exist between patients with indistinguishable disease symptoms, and these unique molecular features may underlie pathogenic heterogeneity. In order to detect molecular dissimilarities between patients and to reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytic procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI). To evaluate this procedure, we compared it with a common used method principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression datasets that added genotype informative was conducted to examine the performance between our procedure and PCA-K. Our results showed that NMF was superior to PCA-K. Additionally, we performed our proposed procedure with a publicly available dataset of Alzheimer’s disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes among patients with LOAD ( ): MS1 ( ), MS2 ( ), and MS3 ( ). ARI was then used to determine the most representative transcripts for each metagene. As results, 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts could be treated as endophenotypes. Our findings revealed that MS1 might have a specific biological characteristic relative to APOE gene comparing to other molecular subtypes and MS2 might be a normal-like subtype. Our proposed procedure provided an alternative approach to investigate the pathogenic mechanism of disease to better understand the relationship between phenotype and genetic information.

Contents

Signature Page---i
Thesis Approval---ii
Acknowledgement---iii
Chinese Abstract---iv
English Abstract---v
List of Figures---vii
List of Tables---ix

Chapter 1. Introduction---1
1.1Background---1
1.2Literature Review---7
1.2.1 Endophenotype approach in the genetic analysis---7
1.2.2 Moleclar subtypes of complex disease---11
1.3 Motivation---12
1.4 Specific Aims---17
1.5 Significance---17
1.6 Thesis organization---18
Chapter 2. Materials and Methods---20
2.1The brief review of non-negative matrix factorization (NMF)---20
2.2Endophenotype-identification analytical procedure---21
2.2.1Molecular subtype construction via NMF---21
2.2.2Informative transcript selection for biological interpretation of metagenes---24
2.3Principal component analysis with K-means clustering (PCA-K)---29
2.4Simulation studies---29
2.5Application to late-onset Alzheimer’s disease (LOAD) dataset---33
Chapter 3. Results---36
3.1Simulation results for comparing NMF to PCA-K in endophenotype study---36
3.2Results for the LOAD dataset: molecular subtypes among patients---37
3.3Results for the LOAD dataset: informative transcript selection for molecular subtypes---38
3.4Results for the LOAD dataset: enrichment pathwayanalysis via MetaCore---40
3.4.1Enriched pathways in metagene1-specific transcripts--40
3.4.2Enriched pathways in metagene2-specific transcripts--41
3.4.3Enriched pathways in metagene3-specific transcripts--42
3.5Application of molecularsubtypes in the LOAD dataset: differential expression analysis---43
3.6The validation of molecular subtypes in the LOAD dataset---45
3.7Metagene expression quantitative trait locus (QTL)mapping---46
Chapter 4. Discussions--48
4.1Discussion for molecular subtypes---48
4.2Discussion for simulation study---48
4.3Discussion for application dataset---51
Chapter 5. Conclusions---58
Reference---61
Appendices
Figures---73
Tables---91
Publication---93

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