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Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.

ABSTRACT: A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

SUBMITTER: Kalesinskas L

PROVIDER: S-EPMC9269905 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.ResultsThree experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.ConclusionsWe developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

Project description:PurposeTo investigate the between-laboratory reproducibility of embryo selection/deselection effectiveness using qualitative and quantitative time-lapse parameters.MethodsA systematic search was performed on MEDLINE, EMBASE, and the Cochrane Library (up to February 2020) without restriction on date, language, document type, and publication status. Measuring outcomes included implantation, blastulation, good-quality blastocyst formation, and euploid blastocyst.ResultsWe detected 6 retrospective cohort studies externally validating the first clinical time-lapse model (Meseguer) emphasizing quantitative parameters, of which 3 (including one involving 2 independent centers) were included for the pooled analysis. Receiver operating characteristics analysis showed reduced predictive power of the model when either including or not including sister clinic validation. Fifteen cohort studies evaluating qualitative parameters were included for meta-analysis, and the mean Newcastle-Ottawa Scale was 5.3. Overall, meta-analysis showed significantly adverse association between the presence of ≥ 1 cleavage abnormalities and embryo implantation rates (11 studies, n = 7266; RR = 0.39[0.28, 0.55]95% CI; I2 = 57%). Further analysis showed adverse impacts of direct cleavage (7 studies, n = 7065; RR = 0.28 [0.15, 0.54] 95% CI; I2 = 46%), reverse cleavage (2 studies, n = 3622; RR = 0.16 [0.03, 0.75] 95% CI; I2 = 0%), chaotic cleavage (2 studies, n = 3643; RR = 0.11 [0.02, 0.69] 95% CI; I2 = 24%), and multinucleation (5 studies, n = 2576; RR = 0.59 [0.50, 0.69] 95% CI; I2 = 0%), but not the < 6 intercellular contact points at the 4-cell stage (1 study, n = 185; RR = 0.17 [0.02, 1.15] 95% CI).ConclusionsQualitative time-lapse parameters are reliably associated with embryo developmental potential among laboratories, whereas the reproducibility of time-lapse embryo selection model that emphasizes quantitative parameters may be compromised when externally applied.

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Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.

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