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

Canzler, Sebastian; Hackermüller, Jörg

doi:10.1101/2020.07.17.208215

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Likewise, others have described the advantages of integrative multi-omics analyses for toxicological questions before [ 159 , 160 ]. Since tools for an integrated pathway enrichment are emerging [ 161 , 162 ], this should be considered in future multi-omics studies.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Karkossa

Raps

Bergen

et al. 2020

IJMS

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Insights into the modes of action (MoAs) of xenobiotics are of utmost importance for the definition of adverse outcome pathways (AOPs), which are essential for a mechanism-based risk assessment. A well-established strategy to reveal MoAs of xenobiotics is the use of omics. However, often an even more comprehensive approach is needed, which can be achieved using multi-omics. Since the immune system plays a central role in the defense against foreign substances and pathogens, with the innate immune system building a first barrier, we systematically reviewed multi-omics studies investigating the effects of xenobiotics on macrophages. Surprisingly, only nine publications were identified, combining proteomics with transcriptomics or metabolomics. We summarized pathways and single proteins, transcripts, or metabolites, which were described to be affected upon treatment with xenobiotics in the reviewed studies, thus revealing a broad range of effects. In summary, we show that macrophages are a relevant model system to investigate the toxicological effects induced by xenobiotics. Furthermore, the multi-omics approaches led to a more comprehensive overview compared to only one omics layer with slight advantages for combinations that complement each other directly, e.g., proteome and metabolome.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Karkossa

Raps

Bergen

et al. 2020

IJMS

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“…Classical analysis would reject these pathways for each block. To get a better biological vision taking into account statistical information shared in several omic blocks and to explain altered pathways between complex phenotypes, we propose here Stouffer's pooling method (see Methods) as seen in different multi-omics tools [16,17]. Therefore, pathways in common between RNAseq and protein enrichment results lead to a multi-omics enrichment table where best pathways are those with best probabilities taking two omic datasets into account.…”

Section: Over Representation Analysis (Ora) Of Deseq2/limma Featuresmentioning

confidence: 99%

Towards an integrative multi-omics workflow

Jeanneret

Gazut

2021

Preprint

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The advent of high-throughput techniques has greatly enhanced biological discovery. Last years, analysis of multi-omics data has taken the front seat to improve physiological understanding. Handling functional enrichment results from various biological data raises practical questions. We propose an integrative workflow to better interpret biological process insights in a multi-omics approach applied to breast cancer data from The Cancer Genome Atlas (TCGA) related to Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). Pathway enrichment by Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) has been conducted with both features information from differential expression analysis or selected features from multi-block sPLS-DA methods. Then, comprehensive comparisons of enrichment results have been carried out by looking at classical enrichment analysis, probabilities pooling by Stouffer's Z scores method and pathways clustering in biological themes. Our work shows that ORA enrichment with selected sPLS-DA features and pathways probabilities pooling by Stouffer's method lead to enrichment maps highly associated to physiological knowledge of IDC or ILC phenotypes, better than ORA and GSEA with differential expression driven features.

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“…Other techniques attempt to account for potential differences that may arise in the results of pathway enrichment analysis by combining gene sets from several pathway databases. For instance, ( 14 ) presented an approach that leverages GSEA to calculate a combined enrichment score for multiple - omics layers using several databases. However, performing pathway enrichment analysis using multiple databases to increase the number of pathways covered can only partially address the challenges associated with variability in results.…”

Section: Introductionmentioning

confidence: 99%

DecoPath: a web application for decoding pathway enrichment analysis

Mubeen

Bharadhwaj

Gadiya

et al. 2021

NAR Genomics and Bioinformatics

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The past decades have brought a steady growth of pathway databases and enrichment methods. However, the advent of pathway data has not been accompanied by an improvement in interoperability across databases, hampering the use of pathway knowledge from multiple databases for enrichment analysis. While integrative databases have attempted to address this issue, they often do not account for redundant information across resources. Furthermore, the majority of studies that employ pathway enrichment analysis still rely upon a single database or enrichment method, though the use of another could yield differing results. These shortcomings call for approaches that investigate the differences and agreements across databases and methods as their selection in the design of a pathway analysis can be a crucial step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run enrichment analysis or directly upload results and facilitate the interpretation of results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at https://decopath.scai.fraunhofer.de, and its source code and documentation can be found on GitHub at https://github.com/DecoPath/DecoPath.

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

Cited by 4 publications

References 27 publications

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

Towards an integrative multi-omics workflow

DecoPath: a web application for decoding pathway enrichment analysis

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