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 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 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 GSEA-InContext Explorer: An interactive visualization tool for putting gene set enrichment analysis results into biological context

2019 ◽  
Author(s):  
Rani K. Powers ◽  
Anthony Sun ◽  
James C. Costello
Keyword(s):  
Statistical Significance ◽  
Null Distribution ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Link Type ◽  
Interactive Interface ◽  
Gene Sets ◽  
Shiny App

AbstractSummaryGSEA-InContext Explorer is a Shiny app that allows users to perform two methods of gene set enrichment analysis (GSEA). The first, GSEAPreranked, applies the GSEA algorithm in which statistical significance is estimated from a null distribution of enrichment scores generated for randomly permuted gene sets. The second, GSEA-InContext, incorporates a user-defined set of background experiments to define the null distribution and calculate statistical significance. GSEA-InContext Explorer allows the user to build custom background sets from a compendium of over 5,700 curated experiments, run both GSEAPreranked and GSEA-InContext on their own uploaded experiment, and explore the results using an interactive interface. This tool will allow researchers to visualize gene sets that are commonly enriched across experiments and identify gene sets that are uniquely significant in their experiment, thus complementing current methods for interpreting gene set enrichment results.Availability and implementationThe code for GSEA-InContext Explorer is available at: https://github.com/CostelloLab/GSEA-InContext_Explorer and the interactive tool is at: http://gsea-incontext_explorer.ngrok.io

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 multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sebastian Canzler ◽  
Jörg Hackermüller
Keyword(s):  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Omics Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Enrichment ◽  
Link Type ◽  
Versatile Tool

Abstract Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here, we present the package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions With we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA.

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 multiGSEA: A GSEA-based pathway enrichment analysis for multi-omics data

2020 ◽  
Author(s):  
Sebastian Canzler ◽  
Jörg Hackermüller
Keyword(s):  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Omics Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Enrichment ◽  
Link Type ◽  
Pathway Databases

AbstractGaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well.In recent years the call for a combined analysis of multiple omics layer became prominent, giving rise to a few multi-omics enrichment tools. Each of which has its own drawbacks and restrictions regarding its universal application.Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layer. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. It is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at Bioconductor: https://bioconductor.org/packages/multiGSEA.

scholarly journals Gene-set Enrichment with Regularized Regression

2019 ◽  
Author(s):  
Tao Fang ◽  
Iakov Davydov ◽  
Daniel Marbach ◽  
Jitao David Zhang
Keyword(s):  
Enrichment Analysis ◽  
Statistical Modelling ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Regularized Regression ◽  
Gene Set Enrichment ◽  
Regression Problem ◽  
Gene Set ◽  
Link Type ◽  
Gene Sets

AbstractMotivationCanonical methods for gene-set enrichment analysis assume independence between gene-sets. In practice, heterogeneous gene-sets from diverse sources are frequently combined and used, resulting in gene-sets with overlapping genes. They compromise statistical modelling and complicate interpretation of results.ResultsWe rephrase gene-set enrichment as a regression problem. Given some genes of interest (e.g.a list of hits from an experiment) and gene-sets (e.g.functional annotations or pathways), we aim to identify a sparse list of gene-sets for the genes of interest. In a regression framework, this amounts to identifying a minimum set of gene-sets that optimally predicts whether any gene belongs to the given genes of interest. To accommodate redundancy between gene-sets, we propose regularized regression techniques such as theelastic net.We report that regression-based results are consistent with established gene-set enrichment methods but more parsimonious and interpretable.AvailabilityWe implement the model ingerr(gene-set enrichment with regularized regression), an R package freely available athttps://github.com/TaoDFang/gerrand submitted toBioconductor.Code and data required to reproduce the results of this study are available athttps://github.com/TaoDFang/GeneModuleAnnotationPaper.ContactJitao David Zhang ([email protected]), Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124, 4070 Basel, Switzerland.

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