學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 基於三級交聯圖與表觀基因特徵分群對基因組切割
Genome segmentation based on the embedding of chromosome contact and ChIP-seq networks作者 張修誠
Chang, Hsiu-Cheng貢獻者 張家銘
Chang, Jia-Ming
張修誠
Chang, Hsiu-Cheng關鍵詞 高通量染色體捕獲技術
染色質免疫沉澱-測序
資訊融合
節點嵌入
分群日期 2024 上傳時間 1-三月-2024 13:42:29 (UTC+8) 摘要 高通量染色體捕獲技術（Hi-C）的全基因組染色體接觸矩陣可用於研究染色體三級結構組織，組織由大至小可分為隔間、子隔間以及拓撲結構域。本研究透過高通量染色體捕獲技術和染色質免疫沉澱-測序（ChIP-seq）兩項技術獲得了基因組的空間結構和基因間相互作用的重要信息，應用圖論的節點嵌入和資訊融合的技術並分群，以高解析度資料處理不同尺度的組織分區，提供更全面的基因組切割分析，透過降維視覺化和量化分析結果呈現有效找出基因體區分。 參考文獻 1. Lieberman-Aiden, Erez, et al. "Comprehensive mapping of long-range interactions reveals folding principles of the human genome." Science 326.5950 (2009): 289-293. 2. Dekker, Job, et al. "Capturing chromosome conformation." Science 295.5558 (2002): 1306-1311. 3. Van Berkum, Nynke L., et al. "Hi-C: a method to study the three-dimensional architecture of genomes." JoVE (Journal of Visualized Experiments) 39 (2010): e1869. 4. Johnson, David S., et al. "Genome-wide mapping of in vivo protein-DNA interactions." Science 316.5830 (2007): 1497-1502. 5. Illumina et al.Pub. No. 770-2007-007 Current as of 26 November 2007. Whole-Genome Chromatin IP Sequencing (ChIP-seq). 6. Rao, Suhas SP, et al. "A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping." Cell 159.7 (2014): 1665-1680. 7. Peifer, Martin, et al. "Telomerase activation by genomic rearrangements in high-risk neuroblastoma." Nature 526.7575 (2015): 700-704. 8. Ashoor, Haitham, et al. "Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data." Nature communications 11.1 (2020): 1173. 9. Tang, Jian, et al. "Line: Large-scale information network embedding." Proceedings of the 24th international conference on world wide web. 2015. 10. Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016. 11. Hou, Chengbin, Shan He, and Ke Tang. "RoSANE: Robust and scalable attributed network embedding for sparse networks." Neurocomputing 409 (2020): 231-243. 12. 吳映函.HiCSeg: an interactive genome segmentation cross samples and species (2021). 13. Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008. 14. Traag, Vincent A., Ludo Waltman, and Nees Jan Van Eck. "From Louvain to Leiden: guaranteeing well-connected communities." Scientific Reports 9.1 (2019): 5233. 15. Knight, Philip A., and Daniel Ruiz. "A fast algorithm for matrix balancing." IMA Journal of Numerical Analysis 33.3 (2013): 1029-1047. 16. Van der Maaten, Laurens, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning research 9.11 (2008). 17. Vinh, Nguyen Xuan, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison: is a correction for chance necessary?." Proceedings of the 26th annual international conference on machine learning. 2009. 18. Rousseeuw, Peter J. "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis." Journal of computational and applied mathematics 20 (1987): 53-65. 19. Caliński, Tadeusz, and Jerzy Harabasz. "A dendrite method for cluster analysis." Communications in Statistics-theory and Methods 3.1 (1974): 1-27. 20. Eigenvector,Juicer.(2017).https://github.com/aidenlab/juicer/wiki/Eigenvector 21. Guo, Kun, et al. "Network Embedding Based on Biased Random Walk for Community Detection in Attributed Networks." IEEE Transactions on Computational Social Systems (2022). 22. Robinson, James T., et al. "Juicebox. js provides a cloud-based visualization system for Hi-C data." Cell systems 6.2 (2018): 256-258. 描述 碩士
國立政治大學
資訊科學系
110753165資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110753165 資料類型 thesis dc.contributor.advisor 張家銘 zh_TW dc.contributor.advisor Chang, Jia-Ming en_US dc.contributor.author (作者) 張修誠 zh_TW dc.contributor.author (作者) Chang, Hsiu-Cheng en_US dc.creator (作者) 張修誠 zh_TW dc.creator (作者) Chang, Hsiu-Cheng en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-三月-2024 13:42:29 (UTC+8) - dc.date.available 1-三月-2024 13:42:29 (UTC+8) - dc.date.issued (上傳時間) 1-三月-2024 13:42:29 (UTC+8) - dc.identifier (其他 識別碼) G0110753165 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150172 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 110753165 zh_TW dc.description.abstract (摘要) 高通量染色體捕獲技術（Hi-C）的全基因組染色體接觸矩陣可用於研究染色體三級結構組織，組織由大至小可分為隔間、子隔間以及拓撲結構域。本研究透過高通量染色體捕獲技術和染色質免疫沉澱-測序（ChIP-seq）兩項技術獲得了基因組的空間結構和基因間相互作用的重要信息，應用圖論的節點嵌入和資訊融合的技術並分群，以高解析度資料處理不同尺度的組織分區，提供更全面的基因組切割分析，透過降維視覺化和量化分析結果呈現有效找出基因體區分。 zh_TW dc.description.tableofcontents 第一章 緒論 1 1.1 高通量染色體捕獲技術 (Hi-C) 1 1.2 染色質免疫沉澱-測序 (ChIP-seq) 1 1.3 基因體隔間與拓撲結構域問題 2 1.4 節點嵌入 4 1.5 資訊融合 5 1.6 基於節點嵌入處理隔間問題 6 第二章 方法 8 2.1 概覽 8 2.2 資料集 9 2.2.1 高通量染色體捕獲技術資料集 9 2.2.2 染色質免疫沉澱-測序資料集 9 2.3 KR正規化 10 2.4 染色質免疫沉澱-測序資料分割 11 2.5 皮爾森相關矩陣 12 2.6 染色質免疫沉澱-測序相似矩陣 12 2.7 資訊融合矩陣 13 2.8 主成分分析 14 2.9 矩陣轉換成圖 14 2.10 節點嵌入與分群 14 2.11 分群與生物標記比較 15 第三章 結果 19 3.1 主成分分析結果再現與改進 19 3.2 染色質免疫沉澱-測序相似矩陣參數分析 19 3.3 資訊融合矩陣參數分析 22 3.4 視覺化結果 22 3.4.1 A/B隔間視覺化 23 3.4.2 子隔間視覺化 26 3.5 量化結果 30 3.5.1 群外指標 30 3.5.2 群內指標 31 3.5.2.1 輪廓係數 31 3.5.2.2 Calinski-Harabasz Index 32 3.5.2.3 群內指標小結 33 3.6 基因切割視覺化 33 3.6.1 染色質免疫沉澱資料分布與基因切割分析 33 3.6.2 基因切割視覺化與分析 35 3.7 基因切割量化 38 第四章 結論與未來展望 40 參考文獻 41 zh_TW dc.format.extent 3656356 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110753165 en_US dc.subject (關鍵詞) 高通量染色體捕獲技術 zh_TW dc.subject (關鍵詞) 染色質免疫沉澱-測序 zh_TW dc.subject (關鍵詞) 資訊融合 zh_TW dc.subject (關鍵詞) 節點嵌入 zh_TW dc.subject (關鍵詞) 分群 zh_TW dc.title (題名) 基於三級交聯圖與表觀基因特徵分群對基因組切割 zh_TW dc.title (題名) Genome segmentation based on the embedding of chromosome contact and ChIP-seq networks en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Lieberman-Aiden, Erez, et al. "Comprehensive mapping of long-range interactions reveals folding principles of the human genome." Science 326.5950 (2009): 289-293. 2. Dekker, Job, et al. "Capturing chromosome conformation." Science 295.5558 (2002): 1306-1311. 3. Van Berkum, Nynke L., et al. "Hi-C: a method to study the three-dimensional architecture of genomes." JoVE (Journal of Visualized Experiments) 39 (2010): e1869. 4. Johnson, David S., et al. "Genome-wide mapping of in vivo protein-DNA interactions." Science 316.5830 (2007): 1497-1502. 5. Illumina et al.Pub. No. 770-2007-007 Current as of 26 November 2007. Whole-Genome Chromatin IP Sequencing (ChIP-seq). 6. Rao, Suhas SP, et al. "A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping." Cell 159.7 (2014): 1665-1680. 7. Peifer, Martin, et al. "Telomerase activation by genomic rearrangements in high-risk neuroblastoma." Nature 526.7575 (2015): 700-704. 8. Ashoor, Haitham, et al. "Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data." Nature communications 11.1 (2020): 1173. 9. Tang, Jian, et al. "Line: Large-scale information network embedding." Proceedings of the 24th international conference on world wide web. 2015. 10. Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016. 11. Hou, Chengbin, Shan He, and Ke Tang. "RoSANE: Robust and scalable attributed network embedding for sparse networks." Neurocomputing 409 (2020): 231-243. 12. 吳映函.HiCSeg: an interactive genome segmentation cross samples and species (2021). 13. Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008. 14. Traag, Vincent A., Ludo Waltman, and Nees Jan Van Eck. "From Louvain to Leiden: guaranteeing well-connected communities." Scientific Reports 9.1 (2019): 5233. 15. Knight, Philip A., and Daniel Ruiz. "A fast algorithm for matrix balancing." IMA Journal of Numerical Analysis 33.3 (2013): 1029-1047. 16. Van der Maaten, Laurens, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning research 9.11 (2008). 17. Vinh, Nguyen Xuan, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison: is a correction for chance necessary?." Proceedings of the 26th annual international conference on machine learning. 2009. 18. Rousseeuw, Peter J. "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis." Journal of computational and applied mathematics 20 (1987): 53-65. 19. Caliński, Tadeusz, and Jerzy Harabasz. "A dendrite method for cluster analysis." Communications in Statistics-theory and Methods 3.1 (1974): 1-27. 20. Eigenvector,Juicer.(2017).https://github.com/aidenlab/juicer/wiki/Eigenvector 21. Guo, Kun, et al. "Network Embedding Based on Biased Random Walk for Community Detection in Attributed Networks." IEEE Transactions on Computational Social Systems (2022). 22. Robinson, James T., et al. "Juicebox. js provides a cloud-based visualization system for Hi-C data." Cell systems 6.2 (2018): 256-258. zh_TW
