BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach

Background: With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses. Results: We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under the curve, bias and MSE for parameter estimates), our simulation results indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missingness due to MAR and MNAR. Further, our approach was competitive with other methods tailored specifically to MNAR in situations where missing data were completely MNAR. Applying our approach to an analysis of metabolomics data from a mouse myocardial infarction revealed several statistically significant metabolites not previously identified that were of direct biological relevance to the study. Conclusions: Our findings demonstrate that BayesMetab has improved performance in imputing the missing values and performing statistical inference compared to other current methods when missing values are due to a mixture of MNAR and MAR. Analysis of real metabolomics data strongly suggests this mixture is likely to occur in practice, and thus, it is important to consider an imputation model that accounts for a mixture of missing data types

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

Methods for detecting gene × gene interaction in multiplex extended pedigrees

Complex diseases are multifactorial in nature and can involve multiple loci with gene × gene and gene × environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene × gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

<p>Abstract</p> <p>Background</p> <p>Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures × time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set.</p> <p>Results</p> <p>We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost.</p> <p>Conclusion</p> <p>Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.</p

Cost effectiveness of adherence to IDSA/ATS guidelines in elderly patients hospitalized for Community-Aquired Pneumonia

A copy of the survey questionnaire given to the expert panel regarding patient utilities. (PDF 8.28 kb

Factors associated with failed treatment : an analysis of 121,744 women embarking on their first IVF cycles

Peer reviewedPublisher PD

Assessing the Quality of Care for Pneumonia in Integrated Community Case Management: A Cross-Sectional Mixed Methods Study

Background Pneumonia is the leading infectious cause of mortality in children under five worldwide. Community-level interventions, such as integrated community case management, have great potential to reduce the burden of pneumonia, as well as other diseases, especially in remote populations. However, there are still questions as to whether community health workers (CHW) are able to accurately assess symptoms of pneumonia and prescribe appropriate treatment. This research addresses limitations of previous studies using innovative methodology to assess the accuracy of respiratory rate measurement by CHWs and provides new evidence on the quality of care given for children with symptoms of pneumonia. It is one of few that assesses CHW performance in their usual setting, with independent re-examination by experts, following a considerable period of time post-training of CHWs. Methods In this cross-sectional mixed methods study, 1,497 CHW consultations, conducted by 90 CHWs in two districts of Luapula province, Zambia, were directly observed, with measurement of respiratory rate for children with suspected pneumonia recorded by video. Using the video footage, a retrospective reference standard assessment of respiratory rate was conducted by experts. Counts taken by CHWs were compared against the reference standard and appropriateness of the treatment prescribed by CHWs was assessed. To supplement observational findings, three focus group discussions and nine in depth interviews with CHWs were conducted. Results and Conclusion The findings support existing literature that CHWs are capable of measuring respiratory rates and providing appropriate treatment, with 81% and 78% agreement, respectively, between CHWs and experts. Accuracy in diagnosis could be strengthened through further training and the development of improved diagnostic tools appropriate for resource-poor settings

The Significance of African Lions for the Financial Viability of Trophy Hunting and the Maintenance of Wild Land

Recent studies indicate that trophy hunting is impacting negatively on some lion populations, notably in Tanzania. In 2004 there was a proposal to list lions on CITES Appendix I and in 2011 animal-welfare groups petitioned the United States government to list lions as endangered under their Endangered Species Act. Such listings would likely curtail the trophy hunting of lions by limiting the import of lion trophies. Concurrent efforts are underway to encourage the European Union to ban lion trophy imports. We assessed the significance of lions to the financial viability of trophy hunting across five countries to help determine the financial impact and advisability of the proposed trade restrictions. Lion hunts attract the highest mean prices (US$24,000–US$71,000) of all trophy species. Lions generate 5–17% of gross trophy hunting income on national levels, the proportional significance highest in Mozambique, Tanzania, and Zambia. If lion hunting was effectively precluded, trophy hunting could potentially become financially unviable across at least 59,538 km2 that could result in a concomitant loss of habitat. However, the loss of lion hunting could have other potentially broader negative impacts including reduction of competitiveness of wildlife-based land uses relative to ecologically unfavourable alternatives. Restrictions on lion hunting may also reduce tolerance for the species among communities where local people benefit from trophy hunting, and may reduce funds available for anti-poaching. If lion off-takes were reduced to recommended maximums (0.5/1000 km2), the loss of viability and reduction in profitability would be much lower than if lion hunting was stopped altogether (7,005 km2). We recommend that interventions focus on reducing off-takes to sustainable levels, implementing age-based regulations and improving governance of trophy hunting. Such measures could ensure sustainability, while retaining incentives for the conservation of lions and their habitat from hunting