Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.
Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.
复制标题
纵向基因表达数据的时间表基因集分析。
DOI：
10.1371/journal.pcbi.1004310
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发表时间：
2015-06
影响因子：
4.3
通讯作者：
Thiébaut R
中科院分区：
文献类型：
--
作者：
Hejblum BP;Skinner J;Thiébaut R
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA provided new insights compared to standard approaches thanks to an increased sensitivity compared to other approaches. Those results highlight the benefits of the TcGSA method for analyzing gene expression dynamics.
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影响因子：
14.9
作者：
Nueda MJ;Carbonell J;Medina I;Dopazo J;Conesa A
通讯作者：
Conesa A
使用解除管制链接路径检测解除管制模块。
影响因子：
3.7
作者：
Hu Y;Gao L;Shi K;Chiu DK
通讯作者：
Chiu DK
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3
作者：
Ackermann, Marit;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
影响因子：
5.8
作者：
Guo, X;Qi, HL;Pan, W
通讯作者：
Pan, W