GAGE: generally applicable gene set enrichment for pathway analysis.
GAGE: generally applicable gene set enrichment for pathway analysis.
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DOI：
10.1186/1471-2105-10-161
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发表时间：
2009-05-27
影响因子：
3
通讯作者：
Woolf PJ
中科院分区：
文献类型：
--
作者：
Luo W;Friedman MS;Shedden K;Hankenson KD;Woolf PJ
基因集分析（GSA）是一种基于途径知识的基因表达数据分析策略。GSA专注于相关基因的集合，并且已经建立了相对于单个基因分析的主要优势，包括更高的鲁棒性、灵敏度和生物相关性。然而，以前的GSA方法使用有限，因为它们不能处理不同样本大小或实验设计的数据集。为了解决这些局限性，我们提出了一种新的GSA方法，称为一般适用的基因集富集（GAGE）。我们成功地将GAGE应用于多个微阵列数据集，具有不同的样本量，实验设计和分析技术。与其他两种常用的GSEA和PAGE GSA方法相比，GAGE显示出显著更好的结果。我们在以下三个方面证明了这种改进：（1）重复研究/实验的一致性;（2）灵敏度和特异性;（3）推断的调控机制的生物学相关性。GAGE揭示了新的和相关的调控机制，从已发表的和以前未发表的微阵列研究。从两个已发表的肺癌数据集，GAGE得出了一个更具凝聚力和预测机制的计划，肺癌的进展和转移。对于以前未发表的BMP6研究，GAGE预测了BMP6诱导成骨细胞分化的新调控机制，包括经典的BMP-TGF β信号传导，JAK-STAT信号传导，Wnt信号传导和雌激素信号传导途径-所有这些都得到了实验文献的支持。GAGE通常适用于不同样本量和实验设计的基因表达数据集。GAGE的表现始终优于两种最常用的GSA方法，并在统计学和生物学上推断出更相关的调节途径。GAGE方法在R中的"gage"包中实现，可在GNU GPL下从。
Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs. To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature. GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from .
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影响因子：
3
作者：
Bussemaker HJ;Ward LD;Boorsma A
通讯作者：
Boorsma A
影响因子：
11.2
作者：
Furge, Kyle A.;Chen, Jindong;Teh, Bin Tean
通讯作者：
Teh, Bin Tean
影响因子：
3
作者：
Kim, SY;Volsky, DJ
通讯作者：
Volsky, DJ
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S
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