Theme discovery from gene lists for identification and viewing of multiple functional groups.
Theme discovery from gene lists for identification and viewing of multiple functional groups.
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DOI：
10.1186/1471-2105-6-162
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发表时间：
2005-06-29
影响因子：
3
通讯作者：
Törönen P
中科院分区：
文献类型：
--
作者：
Pehkonen P;Wong G;Törönen P
基因组时代的高通量方法以基因列表的形式产生大量数据。如果没有先进的计算或生物信息学工具，这些列表很大，很难解释。大多数现有的方法分析基因列表作为一个单一的实体，虽然它是由多个基因组与单独的生物功能相关联。因此，必须在基因列表中定义和可视化具有独特功能的基因组。为了分析基因列表中的功能异质性，我们开发了一种方法，将基因聚类到具有同质功能的组。该方法利用非负矩阵分解（Non-negative Matrix Factorization， NMF）生成不同簇数的多个聚类结果。得到的聚类结果组合成一个简单的图形表示，显示在分析的基因列表中过度代表的功能群。我们在两个数据集上展示了它的性能，并展示了改进现有方法的结果。比较还表明，我们的方法创建了一个更简化的视图，有助于在列表中发现生物主题，并从结果中丢弃了信息较少的类。所提出的方法和相关的软件对于与基因列表相关的生物学功能的识别和解释是有用的，特别是对于大型列表的分析是有用的。
High throughput methods of the genome era produce vast amounts of data in the form of gene lists. These lists are large and difficult to interpret without advanced computational or bioinformatic tools. Most existing methods analyse a gene list as a single entity although it is comprised of multiple gene groups associated with separate biological functions. Therefore it is imperative to define and visualize gene groups with unique functionality within gene lists. In order to analyse the functional heterogeneity within a gene list, we have developed a method that clusters genes to groups with homogenous functionalities. The method uses Non-negative Matrix Factorization (NMF) to create several clustering results with varying numbers of clusters. The obtained clustering results are combined into a simple graphical presentation showing the functional groups over-represented in the analyzed gene list. We demonstrate its performance on two data sets and show results that improve upon existing methods. The comparison also shows that our method creates a more simplified view that aids in discovery of biological themes within the list and discards less informative classes from the results. The presented method and associated software are useful for the identification and interpretation of biological functions associated with gene lists and are especially useful for the analysis of large lists.
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DOI：
10.1073/pnas.0305888101
发表时间：
2004-04-27
影响因子：
11.1
作者：
Thorpe, GW;Fong, CS;Dawes, IW
通讯作者：
Dawes, IW
影响因子：
12.3
作者：
Martin D;Brun C;Remy E;Mouren P;Thieffry D;Jacq B
通讯作者：
Jacq B
影响因子：
--
作者：
Fisher, RA
通讯作者：
Fisher, RA
影响因子：
14.9
作者：
Mewes, HW;Frishman, D;Weil, B
通讯作者：
Weil, B
DOI：
10.1073/pnas.0308531101
发表时间：
2004-03-23
影响因子：
11.1
作者：
Brunet, JP;Tamayo, P;Mesirov, JP
通讯作者：
Mesirov, JP