scholarly journals mCSEA: Detecting subtle differentially methylated regions

2018 ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Víctor González-Rumayor ◽  
Pedro Carmona-Sáez
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Differentially Methylated Regions ◽  
Gene Set Enrichment ◽  
Sibling Pairs ◽  
Complex Disorders ◽  
Obesity And Diabetes

AbstractMotivationThe identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.ResultsWe present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina 450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets, we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.AvailabilitymCSEA is freely available from the Bioconductor [email protected]

scholarly journals mCSEA: detecting subtle differentially methylated regions

2019 ◽  
Vol 35 (18) ◽  
pp. 3257-3262 ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Víctor González-Rumayor ◽  
Pedro Carmona-Sáez
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Differentially Methylated Regions ◽  
Sibling Pairs ◽  
Complex Disorders ◽  
Obesity And Diabetes

Abstract Motivation The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders. Results We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify DMRs from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify DMRs not detected by other methods. Availability and implementation mCSEA is freely available from the Bioconductor repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 434 ◽  
Author(s):  
Daniil S. Wiebe ◽  
Nadezhda A. Omelyanchuk ◽  
Aleksei M. Mukhin ◽  
Ivo Grosse ◽  
Sergey A. Lashin ◽  
...  
Keyword(s):  
Abiotic Factors ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
R Package ◽  
Fold Change ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Go Terms ◽  
Singular Enrichment Analysis

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.

scholarly journals Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13882 ◽  
Author(s):  
Binghuang Cai ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Data ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

Differential gene expression in prostate tissue according to vasectomy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 298-298
Author(s):  
Kathryn M Wilson ◽  
Travis Gerke ◽  
Ericka Ebot ◽  
Jennifer A Sinnott ◽  
Jennifer R. Rider ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prostate Tissue ◽  
Gene Set Enrichment ◽  
Normal Prostate ◽  
Gene Sets ◽  
Normal Prostate Tissue

298 Background: We previously found that vasectomy was associated with an increased risk of prostate cancer, and particularly, risk of lethal prostate cancer in the Health Professionals Follow-up Study (HPFS). However, the possible biological basis for this finding is unclear. In this study, we explored possible biological mechanisms by assessing differences in gene expression in the prostate tissue of men with and without a history of vasectomy prostate cancer diagnosis. Methods: Within the HPFS, vasectomy data and gene expression data (20,254 genes) was available from archival tumor tissue from 263 cases, 124 of whom also had data for adjacent normal tissue. To relate expression of individual genes to vasectomy we used linear regression adjusting for age and year at diagnosis. We ran gene set enrichment analysis to identify pathways of genes associated with vasectomy. Results: Among 263 cases, 67 (25%) reported a vasectomy prior to cancer diagnosis. Mean age at diagnosis was 66 years among men without and 65 years among men with vasectomy. Median time between vasectomy and prostate cancer diagnosis was 25 years. Gene expression in tumor tissue was not associated with vasectomy status. In adjacent normal tissue, three individual genes were associated with vasectomy with Bonferroni-corrected p-values of < 0.10: RAPGEF6, OR4C3, and SLC35F4. Gene set enrichment analysis found five pathways upregulated and seven pathways downregulated in men with vasectomy compared to those without in normal prostate tissue with a FDR < 0.05. Upregulated pathways included several immune-related gene sets and G-protein-coupled receptor gene sets. Conclusions: We identified significant differences in gene expression profiles in normal prostate tissue according to vasectomy status among men treated for prostate cancer. The fact that such differences existed several decades after vasectomy provides support for the idea that vasectomy may play a role in the etiology of prostate cancer.

scholarly journals A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARαKnockout by Gene Set Enrichment Analysis

PPAR Research ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Kan He ◽  
Qishan Wang ◽  
Yumei Yang ◽  
Minghui Wang ◽  
Yuchun Pan
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Enrichment Analysis ◽  
Small Cell ◽  
Gene Set Enrichment Analysis ◽  
Small Cell Lung ◽  
Gene Set Enrichment ◽  
Tissue Specific

Gene expression profiling of PPARαhas been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARαactivation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARαrelated respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARαactivation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARαmay have an important role in prevention of cancer development. 12 PPARαdependent pathways and 4 PPARαindependent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARαregulation but gluconeogenesis and propanoate metabolism independent of PPARαregulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARαindependent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.

scholarly journals PaxtoolsR: Pathway Analysis in R Using Pathway Commons

2015 ◽  
Author(s):  
Augustin Luna ◽  
Özgün Babur ◽  
Bülent Arman Aksoy ◽  
Emek Demir ◽  
Chris Sander
Keyword(s):  
Source Code ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
R Language ◽  
Pathway Data ◽  
Lesser General Public License ◽  
Free Open Source ◽  
General Public License

Purpose: PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language. Features include the extraction, merging, and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract, and retrieve data and integrate this data with local BioPAX datasets. Availability: The PaxtoolsR package is compatible with R 3.1.1 on Windows, Mac OS X, and Linux using Bioconductor 3.0 and is available through the Bioconductor R package repository along with source code and a tutorial vignette describing common tasks, such as data visualization and gene set enrichment analysis. Source code and documentation are at http://bioconductor.org/packages/release/bioc/html/paxtoolsr.html. This plugin is free, open-source and licensed under the GNU Lesser General Public License (LGPL) v3.0.

scholarly journals Gene expression patterns in synchronized islet populations

2018 ◽  
Author(s):  
Nikita Mukhitov ◽  
Michael G. Roper
Keyword(s):  
Gene Expression ◽  
Environmental Changes ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Protein Translation ◽  
Gene Set Enrichment Analysis ◽  
Gene Expression Patterns ◽  
Insulin Synthesis

AbstractIn vivo levels of insulin are oscillatory with a period of ~5-10 minutes, implying that the numerous islets of Langerhans within the pancreas are synchronized. While the synchronizing factors are still under investigation, one result of this behavior is expected to be coordinated intracellular [Ca2+] ([Ca2+]i) oscillations throughout the islet population. The role that coordinated [Ca2+]i oscillations have on controlling gene expression within pancreatic islets was examined by comparing gene expression levels in islets that were synchronized using a low amplitude glucose wave and an unsynchronized population. The [Ca2+]i oscillations in the synchronized population were homogeneous and had a significantly lower drift in their oscillation period as compared to unsynchronized islets. This reduced drift in the synchronized population was verified by comparing the drift of in vivo and in vitro profiles from published reports. Microarray profiling indicated a number of Ca2+-dependent genes were differentially regulated between the two islet populations. Gene set enrichment analysis revealed that the synchronized population had reduced expression of gene sets related to protein translation, protein turnover, energy expenditure, and insulin synthesis, while those that were related to maintenance of cell morphology were increased. It is speculated that these gene expression patterns in the synchronized islets results in a more efficient utilization of intra-cellular resources and response to environmental changes.

Gene expression combined with gene set enrichment analysis to identify markers of vinorelbine efficacy in breast cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21099-e21099
Author(s):  
Robert Audet ◽  
Changyu Shen ◽  
Scooter Willis ◽  
Renata Duchnowska ◽  
Krzysztof Adamowicz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Chromosomal Location ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Molecular Function ◽  
Time To Progression ◽  
Gene Set Enrichment ◽  
Histone Binding

e21099 Background: Vinorelbine (V) induces mitotic arrest and apoptosis but there are limited data on its effect on gene expression in breast cancer clinical setting. Methods: 43 adult female patients with pathologically confirmed breast cancer and locally advanced or metastatic disease were treated with V 25 mg/m2 days 1, 8, 15 of a 28-day cycle. Gene expression was assessed in archival FFPE tissue using the microarray-based DASL assay (cDNA-mediated Annealing, Selection extension and Ligation) and correlated with time-to-progression (TTP). Using a Gene Set Enrichment Analysis (GSEA), groups of genes that share a common molecular function, chromosomal location, or regulation were identified in patients classified as having either a short (S) (n=25) or a long (L) (n=18) time to progression (TTP) divided by the median (72 days). The GSEA software ( http://www.broadinstitute.org/gsea/index.jsp ) was used for the analysis. Results: GSEA focusing on genes grouped according to similar a) molecular function: 16 out of a set of 43 genes involved in histone binding were enriched in group S (p = 0.002), consistent with higher expression in group S of HIST3H2BB and HIST1H3I as well as a nuclear transcription factor promoting their expression. b) transcription factors: 14 out of 47 genes were enriched in group S (p = 0.004) and corresponds to genes with promoter regions that match c-fos serum response element-binding transcription factor that modulates, for example, ABCC1 and ABCB1 (P-gp/MDR1) solute carriers. c) chromosomal location: in group S, genes were enriched on chromosome 11q21 (20 out of 45 genes p = 0.004) and on chromosome 12p12 (14 out of 22 genes p = 0.002). Conclusions: a) the up-regulation of histone binding genes is consonant with recent discovery of high affinity V binding to histones b) the role of P-gp/MDR1 in V transport is well known c) our observations on chromosome 11q21 and12p12 are novel. DASL expression combined with GSEA highlights gene sets that correlate with clinical outcome and may lead to predictive markers of V efficacy. Further confirmatory analysis is needed due to the limitation of small sample size and multiple comparisons.

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