A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis.
A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis.
复制标题
一种用于基于基因集富集分析的差异调节路径的新型算法。
DOI：
10.1093/bioinformatics/btp510
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发表时间：
2009-11-01
期刊：
影响因子：
--
通讯作者：
Lenhof HP
中科院分区：
文献类型：
--
作者：
Keller A;Backes C;Gerasch A;Kaufmann M;Kohlbacher O;Meese E;Lenhof HP
Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks. Results: We present a novel dynamic programming algorithm for detecting deregulated signaling cascades. The so-called FiDePa (Finding Deregulated Paths) algorithm interprets differences in the expression profiles of tumor and normal tissues. It relies on the well-known gene set enrichment analysis (GSEA) and efficiently detects all paths in a given regulatory or signaling network that are significantly enriched with differentially expressed genes or proteins. Since our algorithm allows for comparing a single tumor expression profile with the control group, it facilitates the detection of specific regulatory features of a tumor that may help to optimize tumor therapy. To demonstrate the capabilities of our algorithm, we analyzed a glioma expression dataset with respect to a directed graph that combined the regulatory networks of the KEGG and TRANSPATH database. The resulting glioma consensus network that encompasses all detected deregulated paths contained many genes and pathways that are known to be key players in glioma or cancer-related pathogenic processes. Moreover, we were able to correlate clinically relevant features like necrosis or metastasis with the detected paths. Availability: C++ source code is freely available, BiNA can be downloaded from http://www.bnplusplus.org/. Contact: ack@bioinf.uni-sb.de Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
3
作者：
Küntzer J;Backes C;Blum T;Gerasch A;Kaufmann M;Kohlbacher O;Lenhof HP
通讯作者：
Lenhof HP
影响因子：
5.8
作者：
Al-Shahrour, F;Díaz-Uriarte, R;Dopazo, J
通讯作者：
Dopazo, J
影响因子：
3
作者：
Ackermann, Marit;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
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
影响因子：
9.9
作者：
通讯作者：
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