Comprehensive literature review and statistical considerations for microarray meta-analysis

Begum, Ferdouse; Ghosh, Debashis; Tseng, George C.; Feingold, Eleanor

doi:10.1093/nar/gkr1265

Cited by 403 publications

(334 citation statements)

References 136 publications

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“…Evaluating similarities across microarray studies is an important topic for contemporary biomedical research. Simple Pearson's correlations are widely used, but other sophisticated statistical measures, including that of NextBio, have been proposed to quantify the similarity of any two microarray studies (16). It would be interesting to assess the same datasets with other advanced analytical measures.…”

Section: Discussionmentioning

confidence: 99%

Genomic responses in mouse models greatly mimic human inflammatory diseases

Takao

Miyakawa

2014

Proc. Natl. Acad. Sci. U.S.A.

458

403

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The use of mice as animal models has long been considered essential in modern biomedical research, but the role of mouse models in research was challenged by a recent report that genomic responses in mouse models poorly mimic human inflammatory diseases. Here we reevaluated the same gene expression datasets used in the previous study by focusing on genes whose expression levels were significantly changed in both humans and mice. Contrary to the previous findings, the gene expression levels in the mouse models showed extraordinarily significant correlations with those of the human conditions (Spearman’s rank correlation coefficient: 0.43–0.68; genes changed in the same direction: 77–93%; P = 6.5 × 10−11 to 1.2 × 10−35). Moreover, meta-analysis of those datasets revealed a number of pathways/biogroups commonly regulated by multiple conditions in humans and mice. These findings demonstrate that gene expression patterns in mouse models closely recapitulate those in human inflammatory conditions and strongly argue for the utility of mice as animal models of human disorders.

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Section: Discussionmentioning

confidence: 99%

Genomic responses in mouse models greatly mimic human inflammatory diseases

Takao

Miyakawa

2014

Proc. Natl. Acad. Sci. U.S.A.

458

403

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“…For metaanalysis, we used the Fisher's method with a significance level of P < 0.05 to combine P values from the multiple datasets. Fisher's method is a widely used statistical approach in metaanalysis to combine P values from different studies independently of the sample size (13,47,48). Gene ontology and functional analysis was performed using NetworkAnalyst (14).…”

Section: Methodsmentioning

confidence: 99%

Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson’s disease

Santiago

Potashkin

2015

Proc. Natl. Acad. Sci. U.S.A.

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Environmental and genetic factors are likely to be involved in the pathogenesis of Parkinson's disease (PD), the second most prevalent neurodegenerative disease among the elderly. Networkbased metaanalysis of four independent microarray studies identified the hepatocyte nuclear factor 4 alpha (HNF4A), a transcription factor associated with gluconeogenesis and diabetes, as a central regulatory hub gene up-regulated in blood of PD patients. In parallel, the polypyrimidine tract binding protein 1 (PTBP1), involved in the stabilization and mRNA translation of insulin, was identified as the most down-regulated gene. Quantitative PCR assays revealed that HNF4A and PTBP1 mRNAs were upand down-regulated, respectively, in blood of 51 PD patients and 45 controls nested in the Diagnostic and Prognostic Biomarkers for Parkinson's Disease. These results were confirmed in blood of 50 PD patients compared with 46 healthy controls nested in the Harvard Biomarker Study. Relative abundance of HNF4A mRNA correlated with the Hoehn and Yahr stage at baseline, suggesting its clinical utility to monitor disease severity. Using both markers, PD patients were classified with 90% sensitivity and 80% specificity. Longitudinal performance analysis demonstrated that relative abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during the 3-y followup period. The inverse regulation of HNF4A and PTBP1 provides a molecular rationale for the altered insulin signaling observed in PD patients. The longitudinally dynamic biomarkers identified in this study may be useful for monitoring disease-modifying therapies for PD.Parkinson's disease | HNF4A | PTBP1 | network analysis | blood biomarkers S ubstantial efforts have been devoted to the development of diagnostic strategies for Parkinson's disease (PD). In particular, changes in mRNA from cellular whole blood can facilitate the identification of dysregulated processes and diagnostic biomarkers for PD (1, 2). Several molecular signatures in blood have been identified. For example, 22 unique genes were found differentially expressed in blood of PD patients compared with healthy controls (1). Likewise, specific splice variants in blood were associated with PD in samples obtained from two independent clinical trials (2, 3). In addition, altered expression of the vitamin D receptor (VDR) in blood and reduced plasma levels of 25-hydroxy vitamin D 3 have been associated with PD (1, 4). Furthermore, plasma levels of the epidermal growth factor have been associated with cognitive decline in PD (5).Environmental stressors and genetic factors are most likely involved in the pathogenesis of PD. Among the genetic factors associated with PD, mutations in the gene encoding leucine-rich repeat kinase 2 (LRRK2) are the most common cause of autosomal dominant PD (6) and a considerable risk factor in idiopathic forms of the disease (7,8). Given the complex interaction between environmental and genetic factors in sporadic PD, we integrated four independent microarray s...

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“…Thereby, the fixed thresholds used to define significant differential expression, such as P value and log 2 fold change (FC), greatly influenced the outcome and thus the interpretation. Moreover, intersection of DEG lists performs no real data integration but rather provides a consistency summary (Tseng et al, 2012). When applied to larger numbers of transcriptomic experiments, it also leads to cumbersome and complex visualization outputs.…”

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confidence: 99%

The ROS Wheel: Refining ROS Transcriptional Footprints

et al. 2016

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In the last decade, microarray studies have delivered extensive inventories of transcriptome-wide changes in messenger RNA levels provoked by various types of oxidative stress in Arabidopsis (Arabidopsis thaliana). Previous cross-study comparisons indicated how different types of reactive oxygen species (ROS) and their subcellular accumulation sites are able to reshape the transcriptome in specific manners. However, these analyses often employed simplistic statistical frameworks that are not compatible with large-scale analyses. Here, we reanalyzed a total of 79 Affymetrix ATH1 microarray studies of redox homeostasis perturbation experiments. To create hierarchy in such a high number of transcriptomic data sets, all transcriptional profiles were clustered on the overlap extent of their differentially expressed transcripts. Subsequently, meta-analysis determined a single magnitude of differential expression across studies and identified common transcriptional footprints per cluster. The resulting transcriptional footprints revealed the regulation of various metabolic pathways and gene families. The RESPIRATORY BURST OXIDASE HOMOLOG F-mediated respiratory burst had a major impact and was a converging point among several studies. Conversely, the timing of the oxidative stress response was a determining factor in shaping different transcriptome footprints. Our study emphasizes the need to interpret transcriptomic data sets in a systematic context, where initial, specific stress triggers can converge to common, aspecific transcriptional changes. We believe that these refined transcriptional footprints provide a valuable resource for assessing the involvement of ROS in biological processes in plants.

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Comprehensive literature review and statistical considerations for microarray meta-analysis

Cited by 403 publications

References 136 publications

Genomic responses in mouse models greatly mimic human inflammatory diseases

Genomic responses in mouse models greatly mimic human inflammatory diseases

Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson’s disease

The ROS Wheel: Refining ROS Transcriptional Footprints

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