ArticleBioinformatics (Oxford, England)2011
Generalized random set framework for functional enrichment analysis using primary genomics datasets.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Connecting omics signatures and revealing biological mechanisms with iLINCS.Nature communications · 2022Article
- A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis.Frontiers in genetics · 2019Article
- NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning.Nucleic acids research · 2015Article
- Article
- Expressomal approach for comprehensive analysis and visualization of ligand sensitivities of xenoestrogen responsive genes.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.PLoS computational biology · 2013Article
- Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.Biology direct · 2012Review
- Pathway analysis of expression data: deciphering functional building blocks of complex diseases.PLoS computational biology · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationFunctional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results.
resultsWe developed and validated a new statistical framework, generalized random set (GRS) analysis, for comparing the genomic signatures in two datasets without the need for gene categorization. In our tests, GRS produced correct measures of statistical significance, and it showed dramatic improvement in the statistical power over other methods currently used in this setting. We also developed a procedure for identifying genes driving the concordance of the genomics profiles and demonstrated a dramatic improvement in functional coherence of genes identified in such analysis. AVAILABILITY: GRS can be downloaded as part of the R package CLEAN from http://ClusterAnalysis.org/. An online implementation is available at http://GenomicsPortals.org/.
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