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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳泓熹 | zh_TW |
| dc.contributor.advisor | Steven Hung-Hsi Wu | en |
| dc.contributor.author | 賴以勳 | zh_TW |
| dc.contributor.author | Yi-Syun Lai | en |
| dc.date.accessioned | 2023-07-19T16:14:58Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-07-19 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-04-17 | - |
| dc.identifier.citation | Andermann, T., Antonelli, A., Barrett, R. L., & Silvestro, D. (2022). Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning. Front Plant Sci, 13, 839407. https://doi.org/10.3389/fpls.2022.839407
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Protein Cell, 12(5), 315-330. https://doi.org/10.1007/s13238-020-00724-8 Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015). Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease, 26(1), 27663. Margulis, L. (2008). Symbiotic planet: a new look at evolution. Basic books. McArdle, B. H., & Anderson, M. J. (2001). Fitting multivariate models to community data: a comment on distance‐based redundancy analysis. Ecology, 82(1), 290-297. Mumby, P. J., Clarke, K. R., & Harborne, A. R. (1996). Weighting species abundance estimates for marine resource assessment. Aquatic Conservation: Marine and Freshwater Ecosystems, 6(3), 115-120. https://doi.org/10.1002/(sici)1099-0755(199609)6:3<115::Aid-aqc188>3.0.Co;2-t Nearing, J. T., Douglas, G. M., Hayes, M. G., MacDonald, J., Desai, D. K., Allward, N., Jones, C. M. A., Wright, R. J., Dhanani, A. S., Comeau, A. M., & Langille, M. G. I. (2022). Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun, 13(1), 342. https://doi.org/10.1038/s41467-022-28034-z Padial, J. M., Miralles, A., De la Riva, I., & Vences, M. (2010). The integrative future of taxonomy. Frontiers in zoology, 7(1), 1-14. Prost, V., Gazut, S., & Bruls, T. (2021). A zero inflated log-normal model for inference of sparse microbial association networks. PLoS Comput Biol, 17(6), e1009089. https://doi.org/10.1371/journal.pcbi.1009089 Ricotta, C., & Podani, J. (2017). On some properties of the Bray-Curtis dissimilarity and their ecological meaning. Ecological Complexity, 31, 201-205. https://doi.org/10.1016/j.ecocom.2017.07.003 Salafsky, N., Margoluis, R., Redford, K. H., & Robinson, J. G. (2002). Improving the practice of conservation: a conceptual framework and research agenda for conservation science. Conservation biology, 16(6), 1469-1479. Sokal, R. R., & Sneath, P. H. A. (1963). 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Vegetation of the Siskiyou mountains, Oregon and California. Ecological monographs, 30(3), 279-338. Willis, A. D. (2019). Rarefaction, Alpha Diversity, and Statistics. Front Microbiol, 10, 2407. https://doi.org/10.3389/fmicb.2019.02407 Xia, Y., & Sun, J. (2017). Hypothesis Testing and Statistical Analysis of Microbiome. Genes Dis, 4(3), 138-148. https://doi.org/10.1016/j.gendis.2017.06.001 Xia, Y., Sun, J., & Chen, D.-G. (2018). Statistical analysis of microbiome data with R (Vol. 847). Springer. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87745 | - |
| dc.description.abstract | 在微生物學中，多樣性通常是研究兩個或多個不同區域的微生物群落差異。在分析微生物群落差異時，研究人員會依據不同的生物分類等級(Taxonomic rank)作為研究目標。然而，在不同的生物分類等級所得到的統計分析結果很可能不一致。研究人員往往只關注在特定分類學水平上的微生物群落，而這導致一些重要的微生物群落訊息可能被遺漏或誤判。因此，本論文提出了一種新方法，利用成對組合（Pairwise combination）的概念以及置換多元變異數分析（Permutational multivariate analysis of variance, PERMANOVA）來深入了解不同微生物群落之間的差異。本文利用對數常態分布（log-normal distribution）模擬了兩個不同地區的微生物群落並使用混淆矩陣來展現先前研究方法對模擬資料的結果。基於此結果，我利用成對組合的方法來增加檢定結果，並使用校正演算法提高模型的預測率。其中一個結果顯示，在 delta parameter 參數為1.6的情況下，TPR（True Positive Rate, TPR）從0.643提高至0.907。然而，這同時導致 TNR（True Negative Rate, TNR）從 0.96 降低到 0.703 。校正演算法提供了一個不同的角度去分析微生物群落資料，研究人員必須考慮這些匯總統計數據之間的權衡，並根據他們的研究問題選擇最佳標準。此分析結果只適用在此次模擬資料。模型的整體準確率還需要多不同的模擬方法加以驗證。 | zh_TW |
| dc.description.abstract | In microbiology, diversity is an important concept that refers to the variety of different species or ecosystems present in different region. However, analyzing the difference in microbial community at various taxonomic ranks can yield inconsistent results. Researchers may only focus on a specific taxonomic level, leading to the omission or misjudgment of important microbial community information. Therefore, this paper proposes a new method that utilizes the concept of pairwise combination and permutational multivariate analysis of variance (PERMANOVA) to gain insights into the differences between microbial communities. To demonstrate the effectiveness of this method, the log-normal distribution was used to simulate microbial communities in two different regions, and a confusion matrix was used to show the results of previous research methods on the simulated data. Based on the results, a pairwise combination method was used to increase the accuracy of the tests, and a correction algorithm was employed to improve the model's prediction rate. One of the simulation results shows that when the delta parameter is set to 1.6, the TPR (True Positive Rate) increases from 0.643 to 0.907. Nevertheless, it does come with the cost of reducing TNR (True Negative Rate) from 0.96 to 0.703. The correction algorithms provide a new perspective on analyzing microbial community data, researchers must consider the tradeoff between these summary statistics and select the optimal criteria based on their research questions. The results of this analysis are only applicable to this simulation data. The overall performance of the model needs to be verified through various simulation methods. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-07-19T16:14:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-07-19T16:14:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Taxonomy 1 1.2 Microbiology 2 1.3 DNA Sequencing 3 1.4 Biological Diversity 5 1.5 Microbiome Diversity 6 1.6 Microbiome Analysis 9 1.7 PERMANOVA 11 1.8 The Thesis Objectives 12 Chapter 2 Method 14 2.1 Distance Matrix 14 2.1.1 Bray–Curtis Dissimilarity 14 2.1.2 Zero-Adjusted Coefficient Bray–Curtis 15 2.1.3 Weighted Coefficient Bray–Curtis 15 2.2 One-Way PERMANOVA 17 2.2.1 Background 17 2.2.2 Statistic Method 18 2.3 The Proposed Method 19 2.4 Simulated Studies 22 2.4.1 The Process of Simulated Data 23 2.4.2 Confusion Matrix 25 2.5 Correction Algorithm 27 Chapter 3 Result 29 3.1 Once of Simulated Result 29 3.2 Standard Bray–Curtis Result 31 3.3 The Proportion Weighted Bray-Curtis Result 40 3.4 The Standardized Weighted Bray-Curtis Result 40 3.5 Result in the Correction Algorithm 48 Chapter 4 Discussion 55 4.1 The Importance of Weighted Coefficient in Bray-Curtis 55 4.2 The Trade Off with Correction Algorithm in Confusion Matrix 56 4.3 Limitation and Future Work 57 Chapter 5 Conclusion 59 Reference 61 Supplementary 64 | - |
| dc.language.iso | en | - |
| dc.subject | 微生物學 | zh_TW |
| dc.subject | 置换多元變異數分析 | zh_TW |
| dc.subject | 校正演算法 | zh_TW |
| dc.subject | 多樣性 | zh_TW |
| dc.subject | 成對組合 | zh_TW |
| dc.subject | 生物分類等級 | zh_TW |
| dc.subject | Correction algorithm | en |
| dc.subject | Microbiology | en |
| dc.subject | Taxonomic rank | en |
| dc.subject | Pairwise combination | en |
| dc.subject | Permutational multivariate analysis | en |
| dc.subject | Diversity | en |
| dc.title | 利用成對組合方法檢測兩地區之微生物群落差異 | zh_TW |
| dc.title | Detecting Differences in Microbial Communities Between Two Regions Using Pairwise Combination Method | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉力瑜;陳虹諺;邱春火;高崇峰 | zh_TW |
| dc.contributor.oralexamcommittee | Li-Yu Liu;Hung-Yen Chen;Chun-Huo Chiu;Chung-Feng Kao | en |
| dc.subject.keyword | 多樣性,微生物學,生物分類等級,成對組合,置换多元變異數分析,校正演算法, | zh_TW |
| dc.subject.keyword | Diversity,Microbiology,Taxonomic rank,Pairwise combination,Permutational multivariate analysis,Correction algorithm, | en |
| dc.relation.page | 66 | - |
| dc.identifier.doi | 10.6342/NTU202300722 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-04-18 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 農藝學系 | - |
| 顯示於系所單位： | 農藝學系 | |
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