Biology subjects

White, N. M.

Publications and source records attributed to White, N. M..

3 recordsLinked to original sources

bioRxiv2024

GLMMcosinor: Flexible cosinor modeling with a generalized linear mixed modeling framework to characterize rhythmic time series.

BackgroundModeling rhythmic biological processes, such as gene expression and sleep-wake cycles, is critical for understanding physiological mechanisms and their dysregulation in disease. Traditional cosinor analysis, commonly used to model rhythmic data, assumes Gaussian-distributed residuals and does not account for hierarchical data, limiting its applicability in modern biological datasets. ResultsWe present GLMMcosinor, an R package that integrates cosinor modeling into the Generalized Linear Mixed Modeling (GLMM) framework using glmmTMB. GLMMcosinor enables analysis of a broad spectrum of non-Gaussian and hierarchical data structures, including count, positive-only, and zero-inflated distributions. By incorporating mixed-effects modeling, GLMMcosinor improves parameter estimation and biological interpretability. The package includes functions for group comparisons of rhythmic parameters and visualization tools such as polar and time series plots. Additionally, GLMMcosinor is available as a Shiny app for intuitive, code-free analysis. ConclusionsGLMMcosinor significantly extends the flexibility and scope of rhythmic data analysis by incorporating GLMM functionality. It is freely available on GitHub, CRAN, rOpenSci, and the R-universe, with comprehensive documentation and reproducible examples, making it a robust tool for researchers analyzing complex rhythmic datasets.

bioinformatics↗

bioRxiv2019

Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach

MotivationEpigenome-wide studies are often performed using heterogeneous methylation samples, especially when there is no prior information as to which cell-types are disease associated. While much work has been done in ascertaining cell-type fractions and removing cell-type heterogeneity variation, relatively little work has been done in identifying cell-type specific variation in heterogeneous samples.\n\nResultsIn this paper, we present a Bayesian model-based approach for making cell-type specific inferences in heterogeneous settings, by using a logit-Normal sampling distribution and incorporating a priori knowledge of cell-type lineage. The method is applied to the detection of cell-type specific sex effects in methylation, where cell-type information is present as an independent verification of the results. Panels derived from this method contained more loci where CD8+T, CD19+B and Natural Killer cell-types were differentially methylated. The analysis suggests that an ensemble approach with this method included could be used for discovering cell-type specific methylation changes.\n\nAvailabilityhttps://github.com/danwkenn/Bayes_CDM

bioinformatics↗

bioRxiv2017

Accounting For Cell Lineage And Sex Effects In The Identification Of Cell-Specific DNA Methylation Using A Bayesian Model Selection Algorithm

Cell- and sex-specific differences in DNA methylation are major sources of epigenetic variation in whole blood. Failure to account for these confounders may lead to substantial bias in the identification of differentially methylated CpGs and predicted levels of differential methylation. Previous studies have provided evidence of cell-specific methylation, but all of these have been restricted to the detection of differential methylation in a single cell type. We developed a Bayesian model selection algorithm for the identification of cell-specific methylation profiles that incorporates knowledge of shared cell lineage, to accommodate differential methylation in one or more cell types. Under the proposed methodology, sex-specific differences in methylation by cell type are also assessed. Using publicly available cell-sorted methylation data, we show that 51.3% of female CpG markers and 61.4% of male CpG markers identified were associated with differential methylation in more than one cell type. The impact of cell lineage on differential methylation was also highlighted. An evaluation of sex-specific differences revealed marked differences in CD56+NK methylation, within both single and multi-cell dependent methylation patterns. Our findings demonstrate the need to account for cell lineage in studies of differential methylation and associated sex effects.

genetics↗