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

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.

Download Full-text

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

10.1101/200964 ◽

2017 ◽

Author(s):

Van Du T. Tran ◽

Sébastien Moretti ◽

Alix T. Coste ◽

Sara Amorim-Vaz ◽

Dominique Sanglard ◽

...

Keyword(s):

Metabolic Networks ◽

Fitness Function ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Optimal Order ◽

Gene Set Enrichment ◽

Experimental Conditions ◽

Gene Set ◽

Transcriptomic Data ◽

Public Data

AbstractMotivationGenome-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.ResultsWe 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.AvailabilityThe metaboGSE R package is available at https://cran.r-project.org/web/packages/metaboGSE.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btaa658 ◽

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.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btz073 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3514-3516 ◽

Cited By ~ 2

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.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btz831 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1689-1695 ◽

Cited By ~ 1

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.

Download Full-text

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

BioMed Research International ◽

10.1155/2014/346074 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Chih-Yi Chien ◽

Ching-Wei Chang ◽

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Enrichment Analysis ◽

R Package ◽

Ordinary Least Squares ◽

Gene Set Enrichment Analysis ◽

Gene Set Analysis ◽

Gene Set Enrichment ◽

Experimental Conditions ◽

Gene Set ◽

Gene Sets ◽

Significant Difference

Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes thePvalues and FDR (false discovery rate)q-value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.

Download Full-text

ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.799566 ◽

2022 ◽

Vol 11 ◽

Author(s):

Fahui Liu ◽

Jiadong Liang ◽

Puze Long ◽

Lilan Zhu ◽

Wanyun Hou ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

High Expression ◽

Mrna Levels ◽

Gene Set Enrichment ◽

Prognostic Evaluation ◽

Gene Set ◽

Public Data

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

Download Full-text

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

Journal of Clinical Medicine ◽

10.3390/jcm8101580 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1580 ◽

Cited By ~ 1

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.

Download Full-text

Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

BMC Bioinformatics ◽

10.1186/s12859-020-03929-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mike Fang ◽

Brian Richardson ◽

Cheryl M. Cameron ◽

Jean-Eudes Dazard ◽

Mark J. Cameron

Keyword(s):

Drug Targets ◽

T Regulatory Cells ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Regulatory Cells ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Sets ◽

Gastroenteropancreatic Neuroendocrine Tumor ◽

Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

Download Full-text