Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
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DOI：
10.1073/pnas.1732547100
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发表时间：
2003-09-02
影响因子：
11.1
通讯作者：
Jaakkola, TS
中科院分区：
文献类型：
--
作者：
Bar-Joseph, Z;Gerber, G;Jaakkola, TS
我们提出了一个通用的算法来检测两个非均匀的时间序列数据集之间的差异表达的基因。随着越来越多的高通量生物学数据变得可用，基因组和计算生物学的主要挑战是开发用于比较来自不同实验来源的数据的方法。时间序列全基因组表达数据是一个特别有价值的信息来源，因为它们可以描述一个展开的生物过程，如细胞周期或免疫反应。然而，时间序列表达数据集的比较受到生物学和实验不一致性的阻碍，如采样率的差异，生物过程的时序变化，以及缺乏重复。我们的算法克服了这些困难，通过使用时间序列数据的连续表示，并结合了噪声模型的个别样本的全球差异的措施。我们引入了相应的统计方法来计算这种差异表达措施的意义。我们使用我们的算法来比较野生型和敲除酵母菌株中细胞周期依赖的基因表达。我们的算法确定了一组56个差异表达的基因，这些结果通过使用独立的蛋白质-DNA结合数据进行验证。与以前的方法不同，我们的算法还能够识别22个非细胞周期调控基因的差异表达。这组基因在一组独立的表达实验中显著相关，表明转录因子Fkh 1和Fkh 2在控制酵母细胞活性中的额外作用。
We present a general algorithm to detect genes differentially expressed between two nonhomogeneous time-series data sets. As increasing amounts of high-throughput biological data become available, a major challenge in genomic and computational biology is to develop methods for comparing data from different experimental sources. Time-series whole-genome expression data are a particularly valuable source of information because they can describe an unfolding biological process such as the cell cycle or immune response. However, comparisons of time-series expression data sets are hindered by biological and experimental inconsistencies such as differences in sampling rate, variations in the timing of biological processes, and the lack of repeats. Our algorithm overcomes these difficulties by using a continuous representation for time-series data and combining a noise model for individual samples with a global difference measure. We introduce a corresponding statistical method for computing the significance of this differential expression measure. We used our algorithm to compare cell-cycle-dependent gene expression in wild-type and knockout yeast strains. Our algorithm identified a set of 56 differentially expressed genes, and these results were validated by using independent protein-DNA-binding data. Unlike previous methods, our algorithm was also able to identify 22 non-cell-cycle-regulated genes as differentially expressed. This set of genes is significantly correlated in a set of independent expression experiments, suggesting additional roles for the transcription factors Fkh1 and Fkh2 in controlling cellular activity in yeast.