政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141555

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141555

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141555

Title:	scGHSOM: 單細胞序列與基因編輯資料階層式分群與視覺分析 scGHSOM: Hierarchical clustering and visualization of single-cell and CRISPR data using growing hierarchical SOM
Authors:	温上蓉 Wen, Shang-Jung
Contributors:	郁方張家銘 Yu, Fang Chang, Jia-Ming 温上蓉 Wen, Shang-Jung
Keywords:	非監督式分群單細胞序列 GHSOM CyTOF CRISPR GHSOM Unsupervised clustering scRNA-seq CRISPR CyTOF
Date:	2021
Issue Date:	2022-09-02 14:47:42 (UTC+8)
Abstract:	資料科學應用於生物醫學與研究領域，在近年來已經發展成不可或缺的重要角色。透過分析複雜的基因或細胞的異質資料，得到資料的相關性或關係，進而預測出對應疾病的治療方式。我們將非監督式的階層式分群方法 Growing Hierarchical Self-organizing Map (GHSOM)應用於生物資料，例如：單細胞序列資料及CRISPR基因資料。而為了判別出分群後，群之中的重要屬性，我們提出了重要屬性辨別演算法，此演算法依據「群內變異小」且「群間變異大」的規則來找出的在該群中影響分群結果的重要屬性們。而因為較難呈現與分析GHSOM的階層事分群結果，我們也提出兩個結果視覺化方法，「分群特徵呈現圖(Cluster Feature Map)」及「分群位置分佈圖(Cluster Distribution Map)」。分群特徵呈現圖為一個樹狀結構圖，且以顏色來呈現指定的特徵，如：某特定屬性的值，可以讓使用著很容易地觀察到該特徵在分群結果上的表現；而分群位置分佈圖為呈現出每一個葉群(Leaf cluster)的相對位置。我們希望透過這兩個視覺化呈現方式，能夠不使用任何降維方法，如：UMAP、t-SNE等，就能呈現並分析分群的階層式結果。 GHSOM的階層式結構比起非階層式的分群方法，能顯示更多高維度資料的細節。我們比較了GHSOM與其他七種分群方法，如：ACCENSE、K-means、flowMeans等，且GHSOM在之中表現可圈可點，甚至在內部評估中出眾。外部評估為評估分群結果與資料類型的吻合程度，我們使用ARI分數來實現外部評估，在ARI分數中，GHSOM為0.88，且位居第三名；而內部評估為不參考Label，單純計算群內距離小、群與群距離大的分群乾淨程度，我們使用CH分數來實現內部評估，在CH分數中，GHSOM得到4.2，為所有分群方法中的第一名。透過內、外部評估，顯示了GHSOM在眾多分群方法中是有競爭力的。我們提出綜合視覺化方式來呈現非監督式分群法分群後的基因-細胞依賴性資料結果。在非監督式分群法GHSOM分群資料後，分群特徵呈現圖及分群位置分佈圖能不透過降維方法，呈現出分群結果及其特徵，讓使用者能更一目瞭然階層式分群結果的表現及分佈。 Data science applications in the medical field have been growing and have become an indispensable role in research. Analyzing and learning from historical data on genes and cells provides predictions on their relation for effective treatment. We apply an unsupervised and hierarchical clustering, Growing Hierarchical Self-organizing Map (GHSOM), to investigate biological data such as CyTOF, single-cell sequencing and CRISPR genomic data. To identify significant attributes of clusters, we propose a novel Significant Attributes Identification Algorithm. The algorithm figures out attributes having slight variations within the target cluster and high variations between clusters. Through these significant attributes, we would know that the data in the target cluster is highly affected by those significant attributes. Besides, the hierarchical structure of GHSOM clustering results is hard to be presented and analyzed. We also propose two visualization maps, Cluster Feature Map and Cluster Distribution Map. The cluster feature map shows the hierarchical result in coloring each cluster according to the feature value that we would like to observe (The color can be freely defined). Therefore, it is easy for users to identify the uniqueness of features. In the cluster distribution map, we map leaf clusters as circles on the corresponding positions of GHSOM results. The size of circles represents the data size of the clusters. And the color also can be freely defined to the feature that we would like to observe, such as cell type and certain attribute value. We present the clustering result without dimension reduction techniques such as UMAP and t-SNE.
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Description:	碩士國立政治大學資訊管理學系 108356002
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108356002
Data Type:	thesis
DOI:	10.6814/NCCU202201336
Appears in Collections:	[Department of MIS] Theses

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