scholarly journals ebGSEA: an improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies

2019 ◽  
Vol 35 (18) ◽  
pp. 3514-3516 ◽  
Author(s):  
Danyue Dong ◽  
Yuan Tian ◽  
Shijie C Zheng ◽  
Andrew E Teschendorff
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Additional Tool ◽  
Current State ◽  
Biological Interpretation ◽  
The Given

AbstractMotivationThe biological interpretation of differentially methylated sites derived from Epigenome-Wide-Association Studies (EWAS) remains a significant challenge. Gene Set Enrichment Analysis (GSEA) is a general tool to aid biological interpretation, yet its correct and unbiased implementation in the EWAS context is difficult due to the differential probe representation of Illumina Infinium DNA methylation beadchips.ResultsWe present a novel GSEA method, called ebGSEA, which ranks genes, not CpGs, according to the overall level of differential methylation, as assessed using all the probes mapping to the given gene. Applied on simulated and real EWAS data, we show how ebGSEA may exhibit higher sensitivity and specificity than the current state-of-the-art, whilst also avoiding differential probe representation bias. Thus, ebGSEA will be a useful additional tool to aid the interpretation of EWAS data.Availability and implementationebGSEA is available from https://github.com/aet21/ebGSEA, and has been incorporated into the ChAMP Bioconductor package (https://www.bioconductor.org).Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals ebayGSEA: An improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies

2018 ◽  
Author(s):  
Danyue Dong ◽  
Tian Yuan ◽  
Shijie C. Zheng ◽  
Andrew E. Teschendorff
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Additional Tool ◽  
Link Type ◽  
Current State ◽  
Biological Interpretation ◽  
The Given

AbstractMotivationThe biological interpretation of differentially methylated sites derived from Epigenome-Wide-Association Studies remains a significant challenge. Gene Set Enrichment Analysis (GSEA) is a general tool to help aid biological interpretation, yet its correct and unbiased implementation in the EWAS context is difficult due to the differential probe representation of Illumina Infinium DNA methylation beadchips.ResultsWe present a novel GSEA method, called ebayGSEA, which ranks genes, not CpGs, according to the overall level of differential methylation, as assessed using all the probes mapping to the given gene. Applied on simulated and real EWAS data, we show how ebayGSEA may exhibit higher sensitivity and specificity than the current state-of-the-art, whilst also avoiding differential probe representation bias. Thus, ebayGSEA will be a useful additional tool to aid the interpretation of EWAS data.Availability and implementationebayGSEA is available from https://github.com/aet21/ebayGSEA, and has been incorporated into the ChAMP Bioconductor package (https://www.bioconductor.org).

scholarly journals Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Michal Marczyk ◽  
Agnieszka Macioszek ◽  
Joanna Tobiasz ◽  
Joanna Polanska ◽  
Joanna Zyla
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Gene Set Analysis ◽  
Genome Wide Association Studies ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Genome Wide ◽  
The Impact

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar’s test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

scholarly journals Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2258-2266 ◽  
Author(s):  
Van Du T Tran ◽  
Sébastien Moretti ◽  
Alix T Coste ◽  
Sara Amorim-Vaz ◽  
Dominique Sanglard ◽  
...  
Keyword(s):  
Fitness Function ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Optimal Order ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Public Data

Abstract Motivation Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. Results We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A method for downstream analysis of gene set enrichment results facilitates the biological interpretation of vaccine efficacy studies

2016 ◽  
Author(s):  
Yan Tan ◽  
Jernej Godec ◽  
Felix Wu ◽  
Pablo Tamayo ◽  
Jill P. Mesirov ◽  
...  
Keyword(s):  
Transcriptional Response ◽  
Leading Edge ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Biological Interpretation ◽  
Downstream Analysis

AbstractGene set enrichment analysis (GSEA) is a widely employed method for analyzing gene expression profiles. The approach uses annotated sets of genes, identifies those that are coordinately up‐ or down-regulated in a biological comparison of interest, and thereby elucidates underlying biological processes relevant to the comparison. As the number of gene sets available in various collections for enrichment analysis has grown, the resulting lists of significant differentially regulated gene sets may also become larger, leading to the need for additional downstream analysis of GSEA results. Here we present a method that allows the rapid identification of a small number of co-regulated groups of genes – “leading edge metagenes” (LEMs) - from high scoring sets in GSEA results. LEM are sub-signatures which are common to multiple gene sets and that “explain” their enrichment specific to the experimental dataset of interest. We show that LEMs contain more refined lists of context-dependent and biologically meaningful genes than the parental gene sets. LEM analysis of the human vaccine response using a large database of immune signatures identified core biological processes induced by five different vaccines in datasets from human peripheral blood mononuclear cells (PBMC). Further study of these biological processes over time following vaccination showed that at day 3 post-vaccination, vaccines derived from viruses or viral subunits exhibit patterns of biological processes that are distinct from protein conjugate vaccines; however, by day 7 these differences were less pronounced. This suggests that the immune response to diverse vaccines eventually converge to a common transcriptional response. LEM analysis can significantly reduce the dimensionality of enriched gene sets, improve the identification of core biological processes active in a comparison of interest, and simplify the biological interpretation of GSEA results.Author SummaryGenome-wide expression profiling is a widely used tool to identify biological mechanisms in a comparison of interest. One analytic method, Gene set enrichment analysis (GSEA) uses annotated sets of genes and identifies those that are coordinately up‐ or down-regulated in a biological comparison of interest. This approach capitalizes on the fact that alternations in biological processes often cause the coordinated change of a large number of genes. However, as the number of gene sets available in various collections for enrichment analysis has grown, the resulting lists of significant differentially regulated gene sets may also become larger, leading to the need for additional downstream analysis of GSEA results. Here we present a method that allows the identification of a small number of co-regulated groups of genes – “leading edge metagenes” (LEMs) – from high scoring sets in GSEA results. We show that LEMs contain more refined lists of context-dependent biologically meaningful genes than the parental gene sets and demonstrate the utility of this approach in analyzing the transcriptional response to vaccination. LEM analysis can significantly reduce the dimensionality of enriched gene sets, improve the identification of core biological processes active in a comparison of interest, and facilitate the biological interpretation of GSEA results.

scholarly journals Differential Gene Set Enrichment Analysis: a statistical approach to quantify the relative enrichment of two gene sets

2020 ◽  
Author(s):  
James H Joly ◽  
William E Lowry ◽  
Nicholas A Graham
Keyword(s):  
Synthetic Data ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Relative Enrichment ◽  
Gene Sets ◽  
Differential Gene

Abstract Motivation Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, GSEA cannot examine the enrichment of two gene sets or pathways relative to one another. Here we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that quantifies the relative enrichment of two gene sets. Results After validating the method using synthetic data, we demonstrate that DGSEA accurately captures the hypoxia-induced coordinated upregulation of glycolysis and downregulation of oxidative phosphorylation. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Finally, we demonstrate the application of DGSEA to generate hypotheses about differential metabolic pathway activity in cellular senescence. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of gene sets in transcriptomic data. Availability and implementation DGSEA software and tutorials are available at https://jamesjoly.github.io/DGSEA/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis

2019 ◽  
Vol 36 (6) ◽  
pp. 1689-1695 ◽  
Author(s):  
Abhay Hukku ◽  
Corbin Quick ◽  
Francesca Luca ◽  
Roger Pique-Regi ◽  
Xiaoquan Wen
Keyword(s):  
Hierarchical Model ◽  
Enrichment Analysis ◽  
Real Data ◽  
Bayesian Hierarchical Model ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Gene Set Enrichment ◽  
Bayesian Hierarchical ◽  
Gene Set

Abstract Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. Availability and implementation BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Constellation Map: Downstream visualization and interpretation of gene set enrichment results

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 167 ◽  
Author(s):  
Yan Tan ◽  
Felix Wu ◽  
Pablo Tamayo ◽  
W. Nicholas Haining ◽  
Jill P. Mesirov
Keyword(s):  
Full Advantage ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Link Type ◽  
Gene Sets ◽  
Biological Interpretation

Summary: Gene set enrichment analysis (GSEA) approaches are widely used to identify coordinately regulated genes associated with phenotypes of interest. Here, we present Constellation Map, a tool to visualize and interpret the results when enrichment analyses yield a long list of significantly enriched gene sets. Constellation Map identifies commonalities that explain the enrichment of multiple top-scoring gene sets and maps the relationships between them. Constellation Map can help investigators take full advantage of GSEA and facilitates the biological interpretation of enrichment results. Availability: Constellation Map is freely available as a GenePattern module at http://www.genepattern.org.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

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