Constellation Map: Downstream visualization and interpretation of gene set enrichment results.
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ABSTRACT:
SUBMITTER: Tan Y
PROVIDER: S-EPMC4648224 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Constellation Map: Downstream visualization and interpretation of gene set enrichment results.
Tan Yan Y Wu Felix F Tamayo Pablo P Haining W Nicholas WN Mesirov Jill P JP
F1000Research 20150624
<h4>Unlabelled</h4>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 in ...[more]
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Project description:BackgroundGene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal.Principal findingsTo overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results.ConclusionsEnrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).
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Project description:Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results. Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p. Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d , Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results. Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.
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Project description:The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.
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Project description:High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.
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Project description:MotivationThe elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality.ResultsWe have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20,000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.
| S-EPMC2852214 | biostudies-literature
Project description:Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.
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Project description:BackgroundGene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.ResultsWe developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.ConclusionsThe results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.
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