A method to identify differential expression profiles of time-course gene data with Fourier transformation.
A method to identify differential expression profiles of time-course gene data with Fourier transformation.
复制标题
DOI：
10.1186/1471-2105-14-310
复制
发表时间：
2013-10-18
影响因子：
3
通讯作者：
Kim H
中科院分区：
文献类型：
--
作者：
Kim J;Ogden RT;Kim H
时程基因表达实验是一种越来越流行的探索生物过程的方法。时间基因表达谱提供了基因功能的重要表征，因为生物系统是发育的和动态的。利用这些数据，可以研究基因表达随时间的变化，从而检测差异基因。分析时间序列表达数据的许多早期工作依赖于最初为静态数据开发的方法，因此需要改进方法。由于时间序列表达是一个时间过程，其独特的功能，如连续点之间的自相关性应纳入分析。这项工作的目的是确定随着时间的推移显示不同基因表达谱的基因。我们提出了一个统计过程，发现基因组相似的配置文件使用非参数表示，占数据的自相关。特别是，我们首先代表每个配置文件中的傅立叶基础，然后我们筛选出的基因，没有差异表达的基础上的傅立叶系数。最后，我们在傅里叶域中使用基于模型的方法对剩余的基因图谱进行聚类。我们评估筛选结果的灵敏度，特异性，FDR和FNR，比较高斯过程回归筛选在模拟研究和说明的结果应用到酵母细胞周期的微阵列表达数据与α因子同步。所提出的方法的关键要素：（一）在傅立叶域中的基因谱的表示;（二）自动筛选基因的基础上的傅立叶系数，并考虑到数据中的自相关性，同时控制错误的发现率（FDR）;（三）基于模型的聚类其余的基因谱。使用这种方法，我们确定了一组细胞周期调控的时间过程酵母基因。所提出的方法是通用的，可以潜在地用于识别具有相同模式或生物过程的基因，并帮助面对当前和未来的挑战，数据分析的功能基因组学。
Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.
登录
查看更多内容
影响因子：
5.8
作者：
Datta, S;Satten, GA;Datta, S
通讯作者：
Datta, S
影响因子：
8
作者：
MURTAGH, F;RAFTERY, AE
通讯作者：
RAFTERY, AE
影响因子：
2
作者：
Fraley, C;Raftery, AE
通讯作者：
Raftery, AE
影响因子：
5.8
作者：
Kim, Jaehee;Kim, Haseong
通讯作者：
Kim, Haseong
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y