Gene Expression Data Clustering and Visualization Based on a Binary Hierarchical Clustering Framework

机译：基于二进制层次聚类框架的基因表达数据聚类和可视化

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We describe the use of a binary hierarchical clustering (BHC) framework for clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the K-means algorithm is used to split the data into two classes. Secondly, the Fisher criterion is applied to the classes to assess whether the splitting is acceptable. The algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation. We show that by arranging the BHC clustered gene expression data in a tree structure, we can easily visualize the cluster results. In addition, the tree structure display allows user judgement in finalizing the clustering result using prior biological knowledge.

机译：我们描述了使用二进制层次聚类（BHC）框架对基因表达数据进行聚类。 BHC算法涉及两个主要步骤。首先，使用K-means算法将数据分为两类。其次，将Fisher准则应用于类别以评估拆分是否可以接受。该算法将递归应用于子类，并在无法进一步拆分所有聚类时结束。 BHC不需要知道簇数。它没有对每个聚类或类分布中的样本数量做出任何假设。层次框架自然会导致树结构表示。我们表明，通过以树形结构排列BHC聚类基因表达数据，我们可以轻松地可视化聚类结果。另外，树形结构显示允许用户使用现有的生物学知识来最终确定聚类结果。

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来源
《Asia-Pacific Bioinformatics Conference(APBC 2003); 200302; Adelaide(AU)》|2003年|P.145-152|共8页
会议地点 Adelaide(AU)
作者
Lap Keung SZETO; Alan Wee-Chung LIEW; Hong YAN; Sy-sen TANG;
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作者单位

Department of Computer Engineering and Information Technology City University of Hong Kong 83 Tat Chee Avenue, Kowloon, Hong Kong;

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原文格式 PDF
正文语种 eng
中图分类生物工程学（生物技术）;
关键词
gene expression data analysis; K-means clustering; fisher linear discriminant; binary hierarchical clustering framework;

机译：基因表达数据分析； K-均值聚类； fisher线性判别；二元层次聚类框架;

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专利

1. Gene expression data clustering and visualization based on a binary hierarchical clustering framework [J] . Lap Keung Szeto, Alan Wee-Chung Liew, Hong Yan, Journal of Visual Languages & Computing . 2003,第4期

机译：基于二进制层次聚类框架的基因表达数据聚类和可视化
2. A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings. [J] . Torrente A, Kapushesky M, Brazma A Bioinformatics . 2005,第21期

机译：一种用于比较和可视化分层和平面基因表达数据聚类之间关系的新算法。
3. A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings. [J] . Torrente A, Kapushesky M, Brazma A Bioinformatics . 2005,第21期

机译：一种用于比较和可视化分层和平面基因表达数据聚类之间关系的新算法。
4. Gene expression data clustering and visualization based on a binary hierarchical clustering framework [C] . Lap Keung Szeto, Alan Wee-Chung Liew, Hong Yan, Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 . 2003

机译：基于二进制层次聚类框架的基因表达数据聚类和可视化
5. Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering. [D] . Seo, Jinwook. 2005

机译：多维数据的信息可视化设计：将按功能排列的框架与层次化群集集成在一起。
6. A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data [O] . Audrey Hulot, Denis Laloë, Florence Jaffrézic 2021

机译：从多源数据集成多个分层群集或网络的统一框架
7. Gene Expression Data Clustering and Visualization based on a Binary Hierarchical Clustering Framework [O] . Szeto Lap Keung, Liew Alan Wee-Chung, Yan Hong, 2003

机译：基于二进制层次聚类框架的基因表达数据聚类和可视化

Gene Expression Data Clustering and Visualization Based on a Binary Hierarchical Clustering Framework

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