Kim Jihun, Chung Hee-Joon, Jung Yong, Kim Kack-Kyun, Kim Ju Han
Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, 28 Yongon-dong Chongno-gu, Seoul 110-799, Republic of Korea.
J Biomed Inform. 2008 Apr;41(2):232-41. doi: 10.1016/j.jbi.2007.10.003. Epub 2007 Nov 1.
A challenge in microarray data analysis is to interpret observed changes in terms of biological properties and relationships. One powerful approach is to make associations of gene expression clusters with biomedical ontologies and/or biological pathways. However, this approach evaluates only one cluster at a time, returning long unordered lists of annotations for clusters without considering the overall context of the experiment under investigation.
BioLattice is a mathematical framework based on concept lattice analysis for the biological interpretation of gene expression data. By considering gene expression clusters as objects and associated annotations as attributes and by using set inclusion relationships BioLattice orders them to create a lattice of concepts, providing an 'executive' summary of the experimental context. External knowledge resources such as Gene Ontology trees and pathway graphs can be added incrementally. We propose two quantitative structural analysis methods, 'prominent sub-lattice' and 'core-periphery' analyses, enabling systematic comparison of experimental concepts and contexts. BioLattice is implemented as a web-based utility using Scalable Vector Graphics for interactive visualization. We applied it to real microarray datasets with improved biological interpretations of the experimental contexts.
微阵列数据分析中的一个挑战是根据生物学特性和关系来解释观察到的变化。一种强大的方法是将基因表达簇与生物医学本体和/或生物途径进行关联。然而，这种方法一次只评估一个簇，返回的是簇的注释的长无序列表，而不考虑所研究实验的整体背景。
BioLattice是一个基于概念格分析的数学框架，用于基因表达数据的生物学解释。通过将基因表达簇视为对象，将相关注释视为属性，并使用集合包含关系，BioLattice对它们进行排序以创建概念格，提供实验背景的“执行”摘要。诸如基因本体树和途径图等外部知识资源可以逐步添加。我们提出了两种定量结构分析方法，即“突出子格”和“核心-外围”分析，能够对实验概念和背景进行系统比较。BioLattice作为基于网络的实用工具实现，使用可缩放矢量图形进行交互式可视化。我们将其应用于真实的微阵列数据集，对实验背景进行了改进的生物学解释。
