Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
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DOI：
10.1073/pnas.96.6.2907
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发表时间：
1999-03-16
影响因子：
11.1
通讯作者：
Golub, TR
中科院分区：
文献类型：
--
作者：
Tamayo, P;Slonim, D;Golub, TR
阵列技术使同时监测数千个基因的表达模式变得直接。现在的挑战是解释此类大规模数据集。第一步是提取数据中固有的基因表达的基本模式。本文介绍了自组织图的应用，这是一种数学集群分析，特别适合识别和分类复杂的多维数据中的特征。该方法已在执行分析计算的公共计算机软件包中实现，并提供了简单的数据可视化。为了说明这种分析的价值，该方法应用于四个经过良好研究的模型（HL-60，U937，Jurkat和NB4细胞）中的造血分化。测定了约6,000个人基因的表达模式，并创建了一个在线数据库。 Genecluster用于将基因组织到与生物学相关的簇中，这些簇暗示了有关造血分化的新假设，例如，突出了用于治疗急性临床前临床细胞性白血病的“分化治疗”所涉及的某些基因和途径。
Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.