College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Bioinformatics. 2011 Mar 1;27(5):649-54. doi: 10.1093/bioinformatics/btq714. Epub 2010 Dec 24.
The high redundancy of and high degree of cross-talk between biological pathways hint that a sub-pathway may respond more effectively or sensitively than the whole pathway. However, few current pathway enrichment analysis methods account for the sub-pathways or structures of the tested pathways. We present a sub-pathway-based enrichment approach for identifying a drug response principal network, which takes into consideration the quantitative structures of the pathways.
We validated this new approach on a microarray experiment that captures the transcriptional profile of dexamethasone (DEX)-treated human prostate cancer PC3 cells. Compared with GeneTrail and DAVID, our approach is more sensitive to the DEX response pathways. Specifically, not only pathways but also the principal components of sub-pathways and networks related to prostate cancer and DEX response could be identified and verified by literature retrieval.
生物途径之间的高冗余度和高度交叉对话表明，亚途径可能比整个途径更有效地或更敏感地响应。然而，目前很少有通路富集分析方法考虑到被测试通路的亚通路或结构。我们提出了一种基于亚通路的富集方法，用于识别药物反应的主要网络，该方法考虑了通路的定量结构。
我们在一个微阵列实验中验证了这种新方法，该实验捕获了地塞米松（DEX）处理的人前列腺癌 PC3 细胞的转录谱。与 GeneTrail 和 DAVID 相比，我们的方法对 DEX 反应途径更敏感。具体来说，不仅可以识别和验证与前列腺癌和 DEX 反应相关的途径，还可以识别和验证亚途径和网络的主要成分。
