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gomafunctionalenrichmentanalysistoolbasedongomodules
Huang Qiang; Wu Lingyun; Wang Yong; Zhang Xiangsun
2013
Source Publicationchinesejournalofcancer
ISSN1000-467X
Volume32Issue:4Pages:195
AbstractAnalyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/49424
Collection应用数学研究所
Affiliation中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Huang Qiang,Wu Lingyun,Wang Yong,et al. gomafunctionalenrichmentanalysistoolbasedongomodules[J]. chinesejournalofcancer,2013,32(4):195.
APA Huang Qiang,Wu Lingyun,Wang Yong,&Zhang Xiangsun.(2013).gomafunctionalenrichmentanalysistoolbasedongomodules.chinesejournalofcancer,32(4),195.
MLA Huang Qiang,et al."gomafunctionalenrichmentanalysistoolbasedongomodules".chinesejournalofcancer 32.4(2013):195.
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