Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data
Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data
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DOI：
10.1093/bioinformatics/bti367
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发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Fellenberg, K
中科院分区：
文献类型：
--
作者：
Busold, CH;Winter, S;Fellenberg, K
动机：微阵列数据集的功能解释仍然是一项耗时且具有挑战性的任务。到目前为止，功能类别，是相关的一个或多个实验背景（S）已普遍提取的一组受调控的基因，并在长lists.Results：为了便于解释，我们集成了基因本体论（GO）注释对应分析显示基因，实验条件和基因注释在一个单一的情节。这些图中注释的位置可以直接用于基因簇或实验条件的功能解释，而不需要比较长的注释列表。对应分析不限于可以同时比较的实验条件的数量，即使在复杂的实验设置中也可以轻松识别表征注释。由于可用的注释数据量迅速增加，我们应用注释过滤器。因此，所显示的注释的数量可以显著减少到一组描述性注释，进一步增强了绘图的可解释性。我们验证了从酿酒酵母和人胰腺癌的转录数据的方法。
Motivation: The functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists.Results: To facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas.