Defining the optimal animal model for translational research using gene set enrichment analysis.
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ABSTRACT:
SUBMITTER: Weidner C
PROVIDER: S-EPMC4967938 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC6446501 | biostudies-literature
Project description:MotivationGene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion.ResultsWe demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of genes. Individual residuals, grouped or aggregated by chromosomal loci, indicate problematic samples and potential data-entry errors, and help identify hyperdiploidy as a factor playing a key role in expression for this dataset. Subsequent analysis pinpoints suspected DNA copy number abnormalities of specific samples and chromosomes (most prevalent are chromosomes X, 21 and 14), and also reveals significant expression differences between the hyperdiploid and diploid groups on other chromosomes (most prominently 19, 22, 3 and 13)--differences which are apparently not associated with copy number.AvailabilitySoftware for the statistical tools demonstrated in this article is available as Bioconductor package GSEAlm.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2579710 | biostudies-literature
Project description:Gene set enrichment analysis (GSA) methods have been widely adopted by biological labs to analyze data and generate hypotheses for validation. Most of the existing comparison studies focus on whether the existing GSA methods can produce accurate P-values; however, practitioners are often more concerned with the correct gene-set ranking generated by the methods. The ranking performance is closely related to two critical goals associated with GSA methods: the ability to reveal biological themes and ensuring reproducibility, especially for small-sample studies. We have conducted a comprehensive simulation study focusing on the ranking performance of seven representative GSA methods. We overcome the limitation on the availability of real data sets by creating hybrid data models from existing large data sets. To build the data model, we pick a master gene from the data set to form the ground truth and artificially generate the phenotype labels. Multiple hybrid data models can be constructed from one data set and multiple data sets of smaller sizes can be generated by resampling the original data set. This approach enables us to generate a large batch of data sets to check the ranking performance of GSA methods. Our simulation study reveals that for the proposed data model, the Q2 type GSA methods have in general better performance than other GSA methods and the global test has the most robust results. The properties of a data set play a critical role in the performance. For the data sets with highly connected genes, all GSA methods suffer significantly in performance.
| S-EPMC3929260 | biostudies-literature
Project description:Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.
| S-EPMC10320058 | biostudies-literature
Project description:MotivationGene 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.ResultsWe 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 implementationBAGSE 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 informationSupplementary data are available at Bioinformatics online.
| S-EPMC7523653 | biostudies-literature
Project description:Gene set testing problem has become the focus of microarray data analysis. A gene set is a group of genes that are defined by a priori biological knowledge. Several statistical methods have been proposed to determine whether functional gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to analyzing the dependence structure among gene sets. In this study, we have proposed a novel statistical method of gene set association analysis to identify significantly associated gene sets using the coefficient of intrinsic dependence. The simulation studies show that the proposed method outperforms the conventional methods to detect general forms of association in terms of control of type I error and power. The correlation of intrinsic dependence has been applied to a breast cancer microarray dataset to quantify the un-supervised relationship between two sets of genes in the tumor and non-tumor samples. It was observed that the existence of gene-set association differed across various clinical cohorts. In addition, a supervised learning was employed to illustrate how gene sets, in signaling transduction pathways or subnetworks regulated by a set of transcription factors, can be discovered using microarray data. In conclusion, the coefficient of intrinsic dependence provides a powerful tool for detecting general types of association. Hence, it can be useful to associate gene sets using microarray expression data. Through connecting relevant gene sets, our approach has the potential to reveal underlying associations by drawing a statistically relevant network in a given population, and it can also be used to complement the conventional gene set analysis.
| S-EPMC3597597 | biostudies-literature
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:We present a comprehensive and efficient gene set analysis tool, called 'GeneTrail' that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, 'Over-Representation Analysis' (ORA) comparing a reference set of genes to a test set, and 'Gene Set Enrichment Analysis' (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de.
| S-EPMC1933132 | biostudies-literature
Project description:Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/ ), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.
| S-EPMC9980003 | biostudies-literature
Project description:BACKGROUND:Function genomic studies will generally result in lists of genes that may provide clues for exploring biological questions and discovering unanticipated functions, based on differential gene expression analysis, differential epigenomic analysis or co-expression network analysis. While tools have been developed to identify biological functions that are enriched in the genes sets, there remains a need for comprehensive tools that identify functional enrichment of genes for both model and non-model species from a different function classification perspective. RESULTS:We developed AllEnricher, a tool that calculates gene set function enrichment, with user-defined updatable libraries backing up for both model and non-model species as well as providing comprehensive functional interpretation from multiple dimensions, including GO, KEGG, Reactome, DO and DisGeNET. CONCLUSIONS:AllEnricher incorporates up to date information from different public resources and provides a comprehensive resolution for biologists to make sense out of specific gene sets, making it an advanced open-source tool for gene set function analysis.
| S-EPMC7076970 | biostudies-literature