ArticleScientific reports2016
LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Longitudinal pathway analysis using structural information with case studies in early type 1 diabetes.Scientific reports · 2025Article
- GOAT: efficient and robust identification of gene set enrichment.Communications biology · 2024Article
- Benchmarking enrichment analysis methods with the disease pathway network.Briefings in bioinformatics · 2024Article
- Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data.Nature communications · 2021Article
- SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays.Cell systems · 2021Article
- Popularity and performance of bioinformatics software: the case of gene set analysis.BMC bioinformatics · 2021Article
- Toward a gold standard for benchmarking gene set enrichment analysis.Briefings in bioinformatics · 2021Article
- Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies.Frontiers in genetics · 2021Article
- Gene-set Enrichment with Mathematical Biology (GEMB).GigaScience · 2020Article
- GeneSetCluster: a tool for summarizing and integrating gene-set analysis results.BMC bioinformatics · 2020Article
- Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.Genome biology · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- Identifying disease-associated signaling pathways through a novel effector gene analysis.PeerJ · 2020Article
- Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms.Bioinformatics (Oxford, England) · 2019Article
- FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods.BMC bioinformatics · 2019Article
- Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.BMC genomics · 2018Article
- Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.Bioinformatics (Oxford, England) · 2017Article
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4 authors.
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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.
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