scholarly journals Are females more variable than males in gene expression? Meta-analysis of microarray datasets

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Yuichiro Itoh ◽  
Arthur P. Arnold
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Microarray Datasets

scholarly journals A meta-analysis study of gene expression datasets in mouse liver under PPARα knockout

2013 ◽  
Vol 95 (2-3) ◽  
pp. 78-88 ◽  
Author(s):  
KAN HE ◽  
ZHEN WANG ◽  
QISHAN WANG ◽  
YUCHUN PAN
Keyword(s):  
Gene Expression ◽  
Mouse Liver ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Hepatic Tissue ◽  
Marker Genes ◽  
Specific Marker ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor ◽  
Microarray Datasets

SummaryGene expression profiling of peroxisome-proliferator-activated receptor α (PPARα) has been used in several studies, but there were no consistent results on gene expression patterns involved in PPARα activation in genome-wide due to different sample sizes or platforms. Here, we employed two published microarray datasets both PPARα dependent in mouse liver and applied meta-analysis on them to increase the power of the identification of differentially expressed genes and significantly enriched pathways. As a result, we have improved the concordance in identifying many biological mechanisms involved in PPARα activation. We suggest that our analysis not only leads to more identified genes by combining datasets from different resources together, but also provides some novel hepatic tissue-specific marker genes related to PPARα according to our re-analysis.

An Integrative Gene Expression Microarray Meta-analysis Identifies Host Factors and Key Signatures Involved in Hepatitis B Virus Infection

2020 ◽  
Vol 20 (5) ◽  
pp. 698-707 ◽  
Author(s):  
Mohcine Elmessaoudi-Idrissi ◽  
Marc P. Windisch ◽  
Anass Kettani ◽  
Haya Altawalah ◽  
Pascal Pineau ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatitis B Virus ◽  
Meta Analysis ◽  
Hbv Infection ◽  
Host Factors ◽  
Expression Data ◽  
Chronic Hbv Infection ◽  
B Virus ◽  
Chronic Hbv ◽  
Microarray Datasets

: >Hepatitis B virus (HBV) is a global health concern. Viral and host factors orchestrate the natural history of HBV infection, but the impact of host factors that influence the clinical course of the disease remains poorly understood. The aim of this study was to identify host factors crucial to the HBV life cycle by conducting a meta-analysis utilizing public microarray datasets. Methods: An integrative meta-analysis of expression data from two microarray datasets of HBVinfected liver tissues and healthy uninfected livers was conducted to identify gene expression signatures and overlapping biological processes modulating infection/disease. Results: Using integrative meta-analysis of expression data (INMEX), we identified across two datasets a total of 841 genes differentially expressed during HBV infection, including 473 upregulated and 368 downregulated genes. In addition, through functional enrichment and pathway analysis, we observed that Jak-STAT, TLR, and NF-κB are the most relevant signaling pathways in chronic HBV infection. The network-based meta-analysis identified NEDD8, SKP2, JUN, and HIF1A as the most highly ranked hub genes. Conclusion: Thus, these results may provide valuable information about novel potential host factors modulating chronic HBV infection. Such factors may serve as potential targets for the development of novel therapeutics such as activin receptor-like kinase inhibitors.

scholarly journals Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets

PLoS Medicine ◽  
2008 ◽  
Vol 5 (9) ◽  
pp. e184 ◽  
Author(s):  
Adaikalavan Ramasamy ◽  
Adrian Mondry ◽  
Chris C Holmes ◽  
Douglas G Altman
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Key Issues ◽  
Microarray Datasets

scholarly journals A meta-analysis of Microarray Data is Effective for Identifying Gravity-Sensitive Genes

2021 ◽  
Author(s):  
Yin Liang ◽  
Mengxue Wang ◽  
Yun Liu ◽  
Chen Wang ◽  
Ken Takahashi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Candidate Genes ◽  
Dna Microarray ◽  
Meta Analysis ◽  
Vascular Endothelial Cell ◽  
Gene Expression Pattern ◽  
Expression Level ◽  
Vascular Endothelial ◽  
Microarray Datasets

AbstractGravity affects the function and maintenance of organs, such as bones, muscles, and the heart. Several studies have used DNA microarrays to identify genes with altered expressions in response to gravity. However, it is technically challenging to combine the results from various microarray datasets because of their different data structures. We hypothesized it is possible to identify common changes in gene expression from the DNA microarray datasets obtained under various conditions and methods. In this study, we grouped homologous genes to perform a meta-analysis of multiple vascular endothelial cell and skeletal muscle datasets. According to the t-distributed stochastic neighbor embedding (t-SNE) analysis, the changes in the gene expression pattern in vascular endothelial cells formed specific clusters. We also identified candidate genes in endothelial cells that responded to gravity. Further, we exposed human umbilical vein endothelial cells to simulated microgravity using a clinostat and measured the expression levels of the candidate genes. Gene expression analysis using qRT-PCR revealed that the expression level of the prostaglandin transporter gene SLCO2A1 decreased in response to microgravity, consistent with the meta-analysis of microarray datasets. Furthermore, the direction of gravity affected the expression level of SLCO2A1, buttressing the finding that its expression was affected by gravity. These results suggest that a meta-analysis of DNA microarray datasets may help identify new target genes previously overlooked in individual microarray analyses.

scholarly journals Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

2019 ◽  
Author(s):  
Lavida R. K. Rogers ◽  
Madison Verlinde ◽  
George I. Mias
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Smoking Status ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Chronic Obstructive ◽  
P Value ◽  
Obstructive Pulmonary Disease ◽  
Microarray Datasets ◽  
Microarray Expression

AbstractChronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls. We used pre-existing, publicly available microarray expression datasets to conduct a meta-analysis. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, smoking status and study as effects that also included binary interactions. We found 1,513 statistically significant (Benjamini-Hochberg-adjusted p-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from a Tukey test post-hoc analysis (Benjamini-Hochberg-adjusted p-value <0.05 and 10% two-tailed quantiles of mean differences between COPD and control), to identify 304 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the 304 genes as features, which enabled prediction of disease status with 84% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

Pattern of gene expression in different stages of schizophrenia: Down-regulation of NPTX2 gene revealed by a meta-analysis of microarray datasets

2017 ◽  
Vol 27 (10) ◽  
pp. 1054-1063 ◽  
Author(s):  
Mirko Manchia ◽  
Ignazio S. Piras ◽  
Matthew J. Huentelman ◽  
Federica Pinna ◽  
Clement C. Zai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
Down Regulation ◽  
Microarray Datasets

scholarly journals Consensus on Molecular Subtypes of Ovarian Cancer

2017 ◽  
Author(s):  
Gregory M Chen ◽  
Lavanya Kannan ◽  
Ludwig Geistlinger ◽  
Victor Kofia ◽  
Zhaleh Safikhani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Molecular Subtypes ◽  
Meta Analysis ◽  
Gene Signature ◽  
Ovarian Tumors ◽  
Fitting In ◽  
Microarray Datasets

AbstractINTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.METHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.RESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.CONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

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