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研究生:魏孝全
論文名稱:Hi-C實驗資料正規化
論文名稱(外文):Hi-C data normalization
指導教授:薛慧敏
學位類別:碩士
校院名稱:國立政治大學
系所名稱:統計學系
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
畢業學年度:105
語文別:中文
論文頁數:39
中文關鍵詞:染色體捕捉技術Hi-C實驗資料正規化基因特徵偏差
外文關鍵詞:Chromosome conformation captureHi-C dataNormalizationGenome feature
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本研究探討高通量染色體捕捉技術 (high-throughput chromosome conformation capture, Hi-C) 實驗所產生的關聯矩陣資料之正規化方法。已知該類實驗主要用來測量染色體之間的空間距離,正規化的目的是移除資料中的系統性偏差,本文主要針對基因特徵所造成之偏差。有別於Hu等人 (2012) 所提出的「局部基因特徵正規化法」(local genome feature normalization, LGF法),我們所提出的「二次函數正規化法」(quadratic function normalization, QF法) 建立在更為一般化的二次對數模型與負二項分配假設上。本研究透過模擬實驗以及人類淋巴細胞資料 (GSE18199) 來評估QF法的表現,並且與其他方法比較。在模擬實驗中,我們發現當模型正確時,QF法能有效消除偏差。在實例中,當基因特徵偏差被消除後,則染色體之間的相對距離在重複實驗資料之間有更為一致的結果。另一方面,我們發現實驗所採用的限制酶影響關聯矩陣的結果,而且運用這些正規化方法並不能有效消除限制酶造成的偏差。
Recently, the high-throughput chromosome conformation capture (Hi-C) experiment is developed to explore the three-dimensional structure of genomics. To assess the chromosomal interaction, a contact matrix is produced from a Hi-C experiment. Very often, systematic technical biases appear in the contact matrix and lead to inadequate conclusions. Consequently, data normalization to remove these biases is essential and necessary prior advanced inference. In this research, we propose the so-called quadratic function normalization method, which is a modification of the local genome feature normalization (Hu et al., 2012) by considering a more general model. Simulation studies are conducted to evaluate the proposed method. When the model assumption holds, the proposed method has adequate performance. Further, a Hi-C data set of a human lymphoblastoid cell GSE18199 is employed for a comparison of our method and two existing methods. It’s observed that normalization improves the reproducibility between experimental replicates. However, the effect of normalization is lean in eliminating the bias of restriction enzymes.
第一章 緒論 1
第二章 方法 4
第三章 模擬 10
第四章 實例分析 20
第五章 結論 36
參考資料 38
參考資料
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