Predicting gene ontology from a global meta-analysis of 1-color microarray experiments.
Predicting gene ontology from a global meta-analysis of 1-color microarray experiments.
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DOI：
10.1186/1471-2105-12-s10-s14
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发表时间：
2011-10-18
影响因子：
3
通讯作者：
Wren JD
中科院分区：
文献类型：
--
作者：
Dozmorov MG;Giles CB;Wren JD
用于识别具有高度相似共表达谱的基因的微阵列数据的全局荟萃分析（GMA）正在成为预测基因功能和表型的准确方法，即使在缺乏所分析基因的已发表数据的情况下也是如此。由于三分之一的人类基因尚未被鉴定，这种方法是指导实验和快速了解基因生物学作用的一种有前途的方法。为了预测感兴趣的基因的功能，GMA依赖于一种关联内疚的方法来识别具有已知功能的基因组，这些基因组在不同的实验条件下始终与其共表达，这表明了特定生物学目的的协调调节。我们的目标是定义样本，数据集大小和排名参数如何影响预测性能。从GEO下载13，000个人类1色微阵列用于GMA分析。通过计算100个随机选择的基因的集合的预测功能和注释功能之间的基因本体（GO）树内的距离来基准化预测性能。我们发现，随着更多数据集的添加，新预测函数的数量会增加，但在大约2,000个实验的样本量时开始饱和。对于用于预测函数的基因集，我们发现精度随着集合大小的减小而提高，但相应地召回率较差，并且随着集合大小的增加，召回率和F度量也倾向于增加，但以精度为代价。在50个或更多实验中表达的20，813个基因中，72.5%的基因至少发现了一个预测的GO类别。在没有GO注释的5，720个基因中，4，189个具有至少一个使用前40个共表达基因进行预测分析的预测本体。对于剩余的1,531个没有GO预测或注释的基因，约17%（257个基因）具有足够的共表达数据，但没有统计学上显著过度代表的本体，这表明它们的调控可能更复杂。
Global meta-analysis (GMA) of microarray data to identify genes with highly similar co-expression profiles is emerging as an accurate method to predict gene function and phenotype, even in the absence of published data on the gene(s) being analyzed. With a third of human genes still uncharacterized, this approach is a promising way to direct experiments and rapidly understand the biological roles of genes. To predict function for genes of interest, GMA relies on a guilt-by-association approach to identify sets of genes with known functions that are consistently co-expressed with it across different experimental conditions, suggesting coordinated regulation for a specific biological purpose. Our goal here is to define how sample, dataset size and ranking parameters affect prediction performance. 13,000 human 1-color microarrays were downloaded from GEO for GMA analysis. Prediction performance was benchmarked by calculating the distance within the Gene Ontology (GO) tree between predicted function and annotated function for sets of 100 randomly selected genes. We find the number of new predicted functions rises as more datasets are added, but begins to saturate at a sample size of approximately 2,000 experiments. For the gene set used to predict function, we find precision to be higher with smaller set sizes, yet with correspondingly poor recall and, as set size is increased, recall and F-measure also tend to increase but at the cost of precision. Of the 20,813 genes expressed in 50 or more experiments, at least one predicted GO category was found for 72.5% of them. Of the 5,720 genes without GO annotation, 4,189 had at least one predicted ontology using top 40 co-expressed genes for prediction analysis. For the remaining 1,531 genes without GO predictions or annotations, ~17% (257 genes) had sufficient co-expression data yet no statistically significantly overrepresented ontologies, suggesting their regulation may be more complex.