scholarly journals Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

2019 ◽  
Author(s):  
Lavida R. K. Rogers ◽  
Gustavo de los Campos ◽  
George I. Mias
Keyword(s):  
Influenza Vaccination ◽  
Differentially Expressed Genes ◽  
Influenza Infection ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
P Value ◽  
Expression Data ◽  
Microarray Expression Data ◽  
Microarray Expression ◽  
Age And Sex

ABSTRACTInfluenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination. We also investigate genetic variation due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

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.

scholarly journals Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

2021 ◽  
Author(s):  
Anushri Umesh ◽  
Praveen Kumar Guttula ◽  
Mukesh Kumar Gupta
Keyword(s):  
Molecular Markers ◽  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Mirna Gene ◽  
Bovine Mastitis ◽  
Rank Aggregation ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
P Value ◽  
Expression Levels

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

scholarly journals Meta-analysis of differentially expressed genes in autism based on gene expression data

2015 ◽  
Vol 14 (1) ◽  
pp. 2146-2155 ◽  
Author(s):  
L.F. Ning ◽  
Y.Q. Yu ◽  
E.T. GuoJi ◽  
C.G. Kou ◽  
Y.H. Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Expression Data

scholarly journals Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

2020 ◽  
Author(s):  
Rodrigo Haas Bueno ◽  
Mariana Recamonde-Mendoza
Keyword(s):  
Atrial Fibrillation ◽  
Differentially Expressed Genes ◽  
Complex Disease ◽  
Meta Analysis ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
P Value

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results. Public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each individual dataset, and the results were meta-analytically aggregated using the r-th ordered p-value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis. The meta-analysis of transcriptomes resulted in 589 differentially expressed genes, whose protein-protein interaction network presented 11 hubs-bottlenecks and four main identified functional modules. These modules were enriched for, respectively, 23, 54, 33, and 53 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. Stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hubs-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, we found HSPA5, ANK2, CTNNB1, and VWF. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets and treatments.

scholarly journals Data-Driven Analysis of Age, Sex, and Tissue Effects on Gene Expression Variability in Alzheimer’s Disease

2018 ◽  
Author(s):  
Lavida R.K. Brooks ◽  
George I. Mias
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Meta Analysis ◽  
Tissue Type ◽  
Healthy Controls ◽  
Expression Data ◽  
Mitochondrial Translation ◽  
Microarray Expression Data ◽  
Microarray Expression

ABSTRACTAlzheimer’s disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6thleading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population) and the lack of effective treatments and preventive methods. With an increase in life expectancy, the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal and AD age-related brain degeneration and memory loss. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles.Our study aims to identify temporal changes within gene expression profiles of healthy controls and AD subjects. We conducted a meta-analysis using publicly available microarray expression data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We then performed an analysis of variance, using a linear model that incorporated age, tissue type, sex, and disease state as effects, as well as study to account for batch effects, and including binary interaction between factors. Our results identified 3,735 statistically significant (Bonferroni adjusted p<0.05) gene expression differences between AD and healthy controls, which we filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, transmission across chemical synapses and mitochondrial translation.Overall our approach allowed us to effectively combine multiple available microarray datasets and identify gene expression differences between AD and healthy individuals including full age and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve AD diagnostics and potentially treatment or prevention. (US).

scholarly journals Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Lin Zhao ◽  
Yuhui Li ◽  
Zhen Zhang ◽  
Jing Zou ◽  
Jianfu Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Functional Genes ◽  
P Value

Abstract Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-value &lt; 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

scholarly journals A Complete Summary of Non-Parametric Statistical Methods Used For Biological Microarray Data

2019 ◽  
Vol 8 (4) ◽  
pp. 4995-5002
Keyword(s):  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Statistical Tests ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Parametric Methods ◽  
Experimental Conditions ◽  
Microarray Expression Data ◽  
Microarray Expression ◽  
Non Parametric

Microarray technology is developed as a new powerful biotechnology tool, to analyze the expression profile of more than thousands of genes simultaneously. In recent times, Microarray is the most popular research topic. For extracting the differentially expressed genes from microarray data, numerous types of statistical tests are developed. The focus of microarray analysis is to predict genes that show different expression patterns under two different experimental conditions. The aim of this research paper is to explore various types of non-parametric methods proposed to analyze microarray expression data for predicting those genes which are differentially expressed, and a comparative analysis of various methods has been done. Besides, we also predicted the best condition for each method where they perform better and to investigate the disease development mechanism. Many types of statistical tests have been studied for identifying the differentially expressed genes, only very few studies have compared the performance of these methods. In our study, we extensively study and compare the different types of non-parametric methods.

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