Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence.
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ABSTRACT:
SUBMITTER: Keysers C
PROVIDER: S-EPMC7610527 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail.
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Project description:Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed-form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace's method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes.
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Project description:Background and aimsIt has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data.MethodsSeventy-five effect sizes and their standard errors were extracted from 12 trials. Seventy-three per cent (n = 55) of these were non-significant (i.e. P > 0.05). For each non-significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect.ResultsTwenty per cent (n = 11) of the non-significant Bayes factors were < ⅓ and 3.6% (n = 2) were > 3. The other 76.4% (n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > ⅓); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < ⅓ (n = 20), 62.7% (n = 94) were between ⅓ and 3 and 24.0% (n = 36) were > 3, showing good concordance with the main results.ConclusionsUse of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods.
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Project description:Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.
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Project description:In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.
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Project description:Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is an important task in biomedical studies. We seek to improve the power of such testing methods by leveraging ensemble machine learning methods. Ensemble machine learning methods such as random forest, bagging, and adaptive boosting model the relationship between the outcome and the predictor nonparametrically, while stacking combines the strength of multiple learners. We demonstrate the power of the proposed testing methods through Monte Carlo studies and show the use of the methods by applying them to the immunologic biomarkers dataset from the RV144 HIV vaccine efficacy trial.
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Project description:BackgroundThis study aimed to evaluate the ability of next-generation sequencing (NGS) to conduct preimplantation genetic testing (PGT) for thalassemia using affected embryos.MethodsThis study included data from 36 couples who underwent PGT for thalassemia without probands and relative pedigrees. NGS results were compared with prenatal diagnosis results.ResultsThirty-six couples (29 α-thalassemia and 7 β-thalassemia) underwent 41 PGT cycles (31 α-thalassemia and 10 β-thalassemia). Analysis using NGS produced conclusive results for all biopsied blastocysts (100%, 217/217). One hundred and sixty (73.7%, 160/217) were unaffected by thalassemia. Preimplantation genetic testing for aneuploidy revealed that 112 (70.0%, 112/160) were euploid. Single blastocysts were transferred into the uteri of 34 women (53 frozen embryo transfer [FET] cycles). Thirty-two cycles resulted in clinical pregnancies, with a clinical pregnancy rate of 60.1% (32/53) per FET cycle. Twenty-two cycles (22 couples) resulted in 23 live births, with a live birth rate of 43.4% (23/53; 3 cycles were ongoing pregnancies). All 25 embryos' prenatal diagnosis results and/or thalassemia gene analyses after delivery were concordant with the NGS-PGT results. Seven embryos (21.9%, 7/32) were miscarried before 12 weeks' gestation, and the abortion villus in four showed a normal karyotype and thalassemia results consistent with the NGS-PGT results. Aborted fetus samples from 3 cycles were not available because the pregnancy lasted less than 5 weeks.ConclusionNGS can be used to conduct PGT for thalassemia using affected embryos as a reference.Trial registrationRetrospectively registered.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2021-08-18 | GSE182191 | GEO
Project description:Comparing the gene-expression profiles between biological conditions is useful for understanding gene regulation underlying complex phenotypes. Along this line, analysis of differential co-expression (DC) has gained attention in the recent years, where genes under one condition have different co-expression patterns compared with another. We developed an R package Bayes Factor approach for Differential Co-expression Analysis (BFDCA) for DC analysis. BFDCA is unique in integrating various aspects of DC patterns (including Shift, Cross, and Re-wiring) into one uniform Bayes factor. We tested BFDCA using simulation data and experimental data. Simulation results indicate that BFDCA outperforms existing methods in accuracy and robustness of detecting DC pairs and DC modules. Results of using experimental data suggest that BFDCA can cluster disease-related genes into functional DC subunits and estimate the regulatory impact of disease-related genes well. BFDCA also achieves high accuracy in predicting case-control phenotypes by using significant DC gene pairs as markers. BFDCA is publicly available at http://dx.doi.org/10.17632/jdz4vtvnm3.1.
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Project description:Transposable element (TE) insertions are a source of structural variation and can cause genetic instability and gene expression changes. A host can limit the spread of TEs with various repression mechanisms. Many examples of plant and animal interspecific hybrids show disrupted TE repression leading to TE propagation. Recent studies in yeast did not find any increase in transposition rate in hybrids. However, this does not rule out the possibility that the transcriptional or translational activity of TEs increases following hybridization because of a disruption of the host TE control mechanisms. Thus, whether total expression of a TE family is higher in hybrids than in their parental species remains to be examined. We leveraged publically available RNA-seq and ribosomal profiling data on yeast artificial hybrids of the Saccharomyces genus and performed differential expression analysis of their LTR retrotransposons (Ty elements). Our analyses of total mRNA levels show that Ty elements are generally not differentially expressed in hybrids, even when the hybrids are exposed to a low temperature stress condition. Overall, only 2/26 Ty families show significantly higher expression in the S. cerevisiae × S. uvarum hybrids while there are 3/26 showing significantly lower expression in the S. cerevisiae x S. paradoxus hybrids. Our analysis of ribosome profiling data of S. cerevisiae × S. paradoxus hybrids shows similar translation efficiency of Ty in both parents and hybrids, except for Ty1_cer showing higher translation efficiency. Overall, our results do not support the hypothesis that hybridization could act as a systematic trigger of TE expression in yeast and suggest that the impact of hybridization on TE activity is strain and TE specific.
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