title: LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.
abstract: Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.
authors.affiliation: State Key Laboratory of Genetic Engineering, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200436, P.R. China.
authors.forename: Xinran
authors.initials: X
authors.lastname: Dong
authors.affiliation: State Key Laboratory of Genetic Engineering, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200436, P.R. China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Hao
authors.affiliation: State Key Laboratory of Genetic Engineering, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200436, P.R. China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 100433, P.R. China.|Children's Hospital of Fudan University, Shanghai 200433, P.R. China.
authors.forename: Weidong
authors.initials: W
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000235:genetics* / Q000503:physiopathology; D019985:Benchmarking; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D030541:Databases, Genetic; D066264:Datasets as Topic; D004843:Epistasis, Genetic; D005260:Female; D020869:Gene Expression Profiling; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism