From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems.
From sets to graphs: towards a realistic enrichment analysis of transcriptomic systems.
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从集合到图：转化转录组系统的现实富集分析。
DOI：
10.1093/bioinformatics/btr228
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发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Zimmer R
中科院分区：
文献类型：
--
作者：
Geistlinger L;Csaba G;Küffner R;Mulder N;Zimmer R
动机：目前的基因集富集方法不考虑集成员之间的相互作用和关联。集合成员之间正相关和负相关的相互激活和抑制被忽略了。因此，报告了不一致的法规和无背景的表达变化，因此，结果的生物学解释受到阻碍。结果：我们分析了已建立的基因集富集方法及其在1000个表达数据集的大规模调查结果集。报道的统计学显著的基因组仅表现出观察到的差异表达模式和已知的调控相互作用之间的平均一致性。我们提出了基因图富集分析（GGEA）检测一致和连贯的丰富的基因集，基于来自定向基因调控网络的先验知识。首先，与集合富集方法相比，GGEA提高了成对调节与相应的调节基因和被调节基因对中的个体表达变化的一致性。其次，GGEA产生结果集，其中大部分相关表达变化可以由附近的调节因子（如转录因子）解释，再次改进了基于集合的方法。第三，我们在额外的案例研究中证明，GGEA可以应用于人类的调节途径，在那里它灵敏地检测非常特定的调节过程，这是改变中枢神经系统的肿瘤。GGEA显着增加检测的基因集，其中测得的正相关或负相关的表达模式与定向诱导或抑制关系相一致，从而促进基因表达数据的进一步解释。可用性：该方法和附带的可视化功能已被捆绑到一个R包中，并绑定到一个图形用户界面，银河工作流环境，这是作为一个Web服务器运行。联系人：Ludwig. bio.ifi.lmu.de; Ralf. bio.ifi.lmu.de
Motivation: Current gene set enrichment approaches do not take interactions and associations between set members into account. Mutual activation and inhibition causing positive and negative correlation among set members are thus neglected. As a consequence, inconsistent regulations and contextless expression changes are reported and, thus, the biological interpretation of the result is impeded. Results: We analyzed established gene set enrichment methods and their result sets in a large-scale investigation of 1000 expression datasets. The reported statistically significant gene sets exhibit only average consistency between the observed patterns of differential expression and known regulatory interactions. We present Gene Graph Enrichment Analysis (GGEA) to detect consistently and coherently enriched gene sets, based on prior knowledge derived from directed gene regulatory networks. Firstly, GGEA improves the concordance of pairwise regulation with individual expression changes in respective pairs of regulating and regulated genes, compared with set enrichment methods. Secondly, GGEA yields result sets where a large fraction of relevant expression changes can be explained by nearby regulators, such as transcription factors, again improving on set-based methods. Thirdly, we demonstrate in additional case studies that GGEA can be applied to human regulatory pathways, where it sensitively detects very specific regulation processes, which are altered in tumors of the central nervous system. GGEA significantly increases the detection of gene sets where measured positively or negatively correlated expression patterns coincide with directed inducing or repressing relationships, thus facilitating further interpretation of gene expression data. Availability: The method and accompanying visualization capabilities have been bundled into an R package and tied to a grahical user interface, the Galaxy workflow environment, that is running as a web server. Contact: Ludwig.Geistlinger@bio.ifi.lmu.de; Ralf.Zimmer@bio.ifi.lmu.de
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影响因子：
7
作者：
Lee, HK;Hsu, AK;Pavlidis, P
通讯作者：
Pavlidis, P
影响因子：
14.9
作者：
Faith JJ;Driscoll ME;Fusaro VA;Cosgrove EJ;Hayete B;Juhn FS;Schneider SJ;Gardner TS
通讯作者：
Gardner TS
影响因子：
3.7
作者：
Meier R;Mühlethaler-Mottet A;Flahaut M;Coulon A;Fusco C;Louache F;Auderset K;Bourloud KB;Daudigeos E;Ruegg C;Vassal G;Gross N;Joseph JM
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Joseph JM
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
64.8
作者：
Darios, F;Davletov, B
通讯作者：
Davletov, B