Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells.
Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells.
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DOI：
10.1186/1471-2105-9-s2-s12
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发表时间：
2008-03-26
影响因子：
3
通讯作者：
Weisz A
中科院分区：
文献类型：
--
作者：
Mutarelli M;Cicatiello L;Ferraro L;Grober OM;Ravo M;Facchiano AM;Angelini C;Weisz A
微阵列实验能够同时测量细胞中几乎所有转录物的表达水平，从而提供细胞状态的“分子图片”。另一方面，基因组对药理学或激素刺激的反应是动态分子过程，其中时间影响基因活性和表达。迄今为止，由于只有很少的方法可以正确考虑样本之间的时间关系，因此时间序列中微阵列数据统计分析的潜在用途尚未得到充分开发。我们在这里比较了四种不同的方法来分析来自时间过程 mRNA 表达谱实验的数据，该实验包括雌激素对激素反应性人类乳腺癌细胞的影响的研究。使用创新的 Illumina BeadArray 平台监测基因表达，该平台随机分布在芯片表面的每个探针序列平均有 30-40 个重复。我们展示并讨论了通过将不同的统计方法应用于这些数据集进行连续基因表达分析而获得的结果。还评估了应用于数据的归一化算法以及用于选择差异表达转录本的不同参数或阈值选择的影响。在大多数情况下，我们发现选择对于参数和归一化类型的变化相当稳健。然后，我们确定了哪些基因的表达谱随着时间的推移受到激素治疗的显着影响。最终的差异表达基因列表进行了功能类型的聚类分析，以识别具有相似调控动态的基因组。提出了几种处理时间序列基因表达数据的方法，包括评估所应用的不同方法的优点和缺点。由此产生的数据分析方案用于表征人类乳腺癌 ZR-75.1 细胞在整个细胞周期中雌激素诱导的基因表达变化。
Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples. We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics. Several methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.