Showing papers on "Covariance mapping published in 2019"

A marginalized multilevel model for bivariate longitudinal binary data

Abstract: This study considers analysis of bivariate longitudinal binary data. We propose a model based on marginalized multilevel model framework. The proposed model consists of two levels such that the first level associates the marginal mean of responses with covariates through a logistic regression model and the second level includes subject/time specific random intercepts within a probit regression model. The covariance matrix of multiple correlated time-specific random intercepts for each subject is assumed to represent the within-subject association. The subject-specific random effects covariance matrix is further decomposed into its dependence and variance components through modified Cholesky decomposition method and then the unconstrained version of resulting parameters are modelled in terms of covariates with low-dimensional regression parameters. This provides better explanations related to dependence and variance parameters and a reduction in the number of parameters to be estimated in random effects covariance matrix to avoid possible identifiability problems. Marginal correlations between responses of subjects and within the responses of a subject are derived through a Taylor series-based approximation. Data cloning computational algorithm is used to compute the maximum likelihood estimates and standard errors of the parameters in the proposed model. The validity of the proposed model is assessed through a Monte Carlo simulation study, and results are observed to be at acceptable level. Lastly, the proposed model is illustrated through Mother’s Stress and Children’s Morbidity study data, where both population-averaged and subject-specific interpretations are drawn through Emprical Bayes estimation of random effects.

Partial covariance two-dimensional mass spectrometry for determination of biomolecular primary structure

Abstract: Mass spectrometry (MS) is used widely in biomolecular structural analysis and is particularly dominant in the study of proteins. Despite its considerable power, state-of-the-art protein MS frequently suffers from limited reliability of spectrum-to-structure assignments. This could not be solved fully by the dramatic increase in mass accuracy and resolution of modern MS instrumentation or by the introduction of new fragmentation methods. Here we present a new kind of two-dimensional mass spectrometry for high fidelity determination of a biomolecular primary structure based on partial covariance mapping. Partial covariance two-dimensional mass spectrometry (pC-2DMS) detects intrinsic statistical correlations between biomolecular fragments originating from the same or consecutive decomposition events. This enables identification of pairs of ions produced along the same fragmentation pathway of a biomolecule across its entire fragment mass spectrum. We demonstrate that the fragment-fragment correlations revealed by pC-2DMS provide much more specific information on the amino acid sequence and its covalent modifications than the individual fragment mass-to-charge ratios on which standard one-dimensional MS is based. We illustrate the power of pC-2DMS by using it to resolve structural isomers of combinatorially modified histone peptides inaccessible to standard MS.