scholarly journals GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis

2019 ◽  
Vol 36 (2) ◽  
pp. 487-495 ◽  
Author(s):  
Adib Shafi ◽  
Tin Nguyen ◽  
Azam Peyvandipour ◽  
Sorin Draghici
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Gene Signature ◽  
Supplementary Information ◽  
Biological Phenomena ◽  
Gene Level ◽  
Public Repositories ◽  
The Individual ◽  
Stouffer's Method ◽  
Disease Signatures

Abstract Motivation Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher’s method, Stouffer’s method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power. Results Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer’s disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data

2012 ◽  
Vol 9 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Jinlu Cai ◽  
Henry L. Keen ◽  
Curt D. Sigmund ◽  
Thomas L. Casavant
Keyword(s):  
Microarray Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Microarray Data Analysis ◽  
Combined Approach ◽  
Linear Modeling ◽  
Modeling Process ◽  
Genome Wide ◽  
Gene Level

Summary Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

Power-Enhanced Funnel Plots for Meta-Analysis

2020 ◽  
Vol 228 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Relevant Information ◽  
Selective Reporting ◽  
Graphical Display ◽  
Funnel Plot ◽  
Evidential Value ◽  
Graphical Displays ◽  
Funnel Plots ◽  
Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

Replication of the Superstition and Performance Study by

2014 ◽  
Vol 45 (3) ◽  
pp. 239-245 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Tracy L. Caldwell
Keyword(s):  
Task Difficulty ◽  
Statistical Power ◽  
Meta Analysis ◽  
A Priori ◽  
Significant Heterogeneity ◽  
Performance Study ◽  
Improve Performance ◽  
Research Designs ◽  
Series Of Experiments ◽  
And Performance

A recent series of experiments suggests that fostering superstitions can substantially improve performance on a variety of motor and cognitive tasks ( Damisch, Stoberock, & Mussweiler, 2010 ). We conducted two high-powered and precise replications of one of these experiments, examining if telling participants they had a lucky golf ball could improve their performance on a 10-shot golf task relative to controls. We found that the effect of superstition on performance is elusive: Participants told they had a lucky ball performed almost identically to controls. Our failure to replicate the target study was not due to lack of impact, lack of statistical power, differences in task difficulty, nor differences in participant belief in luck. A meta-analysis indicates significant heterogeneity in the effect of superstition on performance. This could be due to an unknown moderator, but no effect was observed among the studies with the strongest research designs (e.g., high power, a priori sampling plan).

Statistical Power in Meta-Analysis

2006 ◽  
Author(s):  
Guy Cafri ◽  
Michael T. Brannick ◽  
Jeffrey Kromrey
Keyword(s):  
Statistical Power ◽  
Meta Analysis

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

scholarly journals Genetic Susceptibility to Periodontal Disease in Down Syndrome: A Case-Control Study

2021 ◽  
Vol 22 (12) ◽  
pp. 6274
Author(s):  
María Fernández ◽  
Alicia de de Coo ◽  
Inés Quintela ◽  
Eliane García ◽  
Márcio Diniz-Freitas ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Metabolic Pathways ◽  
Nominal Significance ◽  
Severe Periodontitis ◽  
Gene Level ◽  
Individual Marker ◽  
The Individual ◽  
Control Study ◽  
Marker Level

Severe periodontitis is prevalent in Down syndrome (DS). This study aimed to identify genetic variations associated with periodontitis in individuals with DS. The study group was distributed into DS patients with periodontitis (n = 50) and DS patients with healthy periodontium (n = 36). All samples were genotyped with the “Axiom Spanish Biobank” array, which contains 757,836 markers. An association analysis at the individual marker level using logistic regression, as well as at the gene level applying the sequence kernel association test (SKAT) was performed. The most significant genes were included in a pathway analysis using the free DAVID software. C12orf74 (rs4315121, p = 9.85 × 10−05, OR = 8.84), LOC101930064 (rs4814890, p = 9.61 × 10−05, OR = 0.13), KBTBD12 (rs1549874, p = 8.27 × 10−05, OR = 0.08), PIWIL1 (rs11060842, p = 7.82 × 10−05, OR = 9.05) and C16orf82 (rs62030877, p = 8.92 × 10−05, OR = 0.14) showed a higher probability in the individual analysis. The analysis at the gene level highlighted PIWIL, MIR9-2, LHCGR, TPR and BCR. At the signaling pathway level, PI3K-Akt, long-term depression and FoxO achieved nominal significance (p = 1.3 × 10−02, p = 5.1 × 10−03, p = 1.2 × 10−02, respectively). In summary, various metabolic pathways are involved in the pathogenesis of periodontitis in DS, including PI3K-Akt, which regulates cell proliferation and inflammatory response.

Abstract P311: Effect of Non-Invasive Imaging on Cardiovascular Risk Factors and Downstream Testing: A Meta-Analysis of Randomized Controlled Trials

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Seamus P Whelton ◽  
Khurram Nasir ◽  
Michael J Blaha ◽  
Daniel S Berman ◽  
Roger S Blumenthal
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Statistical Power ◽  
Controlled Trial ◽  
Meta Analysis ◽  
Cardiovascular Imaging ◽  
Significant Trend ◽  
Inclusion Criteria ◽  
Non Invasive ◽  
Randomized Controlled

Introduction: Non-invasive cardiovascular imaging has been proposed as a method to improve risk stratification and motivate improved patient and physician risk factor modification. Despite increasing use of these technologies there remains limited evidence documenting its effect on downstream testing and improvement in risk factor control. Hypothesis: Addition of the EISNER study to a prior meta-analysis will improve statistical power to demonstrate the downstream consequences of non-invasive cardiovascular imaging. Methods: A comprehensive literature search of the MEDLINE database (1966 through July 2011) was conducted. Major inclusion criteria required: 1) randomized controlled trial design, 2) participants with no known history of coronary heart disease or stroke, and 3) comparison of a group provided with results of a non-invasive imaging scan versus those without results. A total of eight trials with 4,084 participants met the inclusion criteria for this analysis. We analyzed the data using a random effects model to allow for heterogeneity. Results: Among imaging groups there was a significant increase in prescribing for statins (RR, 1.15; 95% CI, 1.01–1.32) and a non-significant trend for increased prescription of aspirin (RR, 1.15; 95% CI, 0.97–1.35), ACE/ARB (RR, 1.12; 95% CI, 0.96–1.31), and insulin (RR, 1.54; 95% CI, 0.75–3.18). There was a non-significant trend towards increased smoking cessation (RR, 1.35; 95% CI, 0.88–2.08). For downstream outcomes there was a non-significant increase in coronary angiography (RR, 1.20; 95% CI, 0.92–1.57), but not for revascularization (RR, 0.92; 95% CI, 0.55–1.53). There was no significant effect of imaging on the change in traditional risk factors. Limitations: There remains a limited number of trials in this important area. Therefore, trials included in this analysis use a variety of different imaging modalities and we were not able to pool the results based on appropriate clinical action (intensification at high risk and reduction at low risk). Conclusions: Non-invasive cardiovascular imaging leads to increased statin use, but associations with other downstream treatments and change in risk factors are not statistically significant. Our results highlight the limited amount of data for describing the downstream consequences after CAC testing.

Identification of a 15 DNA Damage Repair–Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Linping Gu ◽  
Yuanyuan Xu ◽  
Hong Jian
Keyword(s):  
Prognostic Model ◽  
Molecular Subtypes ◽  
Prognostic Biomarker ◽  
Gene Signature ◽  
Supplementary Information ◽  
Related Gene ◽  
Accurate Evaluation ◽  
Damage Repair ◽  
Related Feature ◽  
Cluster 2

Background: Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment. Introduction: To identify a reliable DDR-related gene signature as an independent predictor of LUAD. Methods: DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan–Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis. Results: Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 – 4, T2–T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

Metastudie industrieller Energieflexibilität*/Meta-analysis of industrial energy flexibility - An approach for optimizing the identification of energy flexibility potentials

2017 ◽  
Vol 107 (09) ◽  
pp. 610-616
Author(s):  
S. Eisenhauer ◽  
F. Zimmermann ◽  
M. Reichart ◽  
P. Accordi ◽  
A. Prof. Sauer
Keyword(s):  
Production System ◽  
Meta Analysis ◽  
German Industry ◽  
Wird Eine ◽  
Bottom Up ◽  
Systematic Analysis ◽  
Industry Level ◽  
Large Spread ◽  
Industrial Energy ◽  
The Individual

Bisherige Studien über energetische Flexibilität in der deutschen Industrie weisen das vorhandene Flexibilitätspotenzial mit hoher Streuung aus. Diese Arbeit analysiert relevante Studien in Bezug auf deren Annahmen und Vorgehensweise. Aufbauend auf den bisherigen Vorgehensweisen wird ein Ansatz zur Erhebung der Daten im Produktionssystem vorgestellt. Des Weiteren wird eine Methode zur Aggregation der Daten hoch bis auf Branchenebene entwickelt.   Previous studies on the energetic flexibility of German industry show potentials with a large spread. Therefore, in this article, a systematic analysis of the individual studies and an evaluation of the indicated flexibility potentials are carried out. Based on the existing methods, a bottom-up approach for collecting the data in the production system and the aggregation up to the industry level is presented.

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