Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression.
Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression.
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DOI：
10.1093/dnares/dsp016
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Kinoshita K
中科院分区：
文献类型：
--
作者：
Obayashi T;Kinoshita K
关于基因共表达的信息对预测基因功能是有用的。基于基因芯片平台测量的大量可公开获得的基因表达数据，已经建立了几个用于在模式生物中基因共表达的数据库。在这些数据库中，基因表达模式的皮尔逊相关系数(PCCs)被广泛用作基因共表达的衡量标准。尽管共表达度量或基因芯片汇总方法会影响基因共表达数据库的性能，但以前对这些计算过程的研究只用少量样本和特定物种进行了测试。为了评估共表达措施的有效性，需要使用大规模微阵列数据进行评估。我们首先考察了PCC的特征，发现提取功能相关基因的最佳PCC阈值受基因表达数据库构建方法和目标基因功能的影响。此外，我们发现，当我们使用相关等级而不是相关值时，这个问题可以被克服。这一观察结果通过四个物种的大规模基因表达数据进行了评估：拟南芥、人类、小鼠和大鼠。
Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson's correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat.
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