Lingsong Meng

This paper introduces a Bayesian framework for detecting circadian rhythmicity biomarkers across the genome, showing improved false discovery rate control and performance in RNA-sequencing data.

CosinorTest is an R Shiny app for circadian and differential analysis of transcriptomic data using Cosinor models, offering tools for rhythmicity detection, differential pattern assessment, and visualization.

This study identifies age-dependent and tissue-specific changes in the circadian transcriptome of male mice, showing reduced rhythmic gene expression and increased variability in aged tissues.

The paper develops linear mixed model methods to detect circadian rhythmicity and differential circadian patterns using repeated measurements, showing improved statistical power and validity.

This paper introduces likelihood-based methods to detect circadian rhythmicity and differential circadian patterns in transcriptomic data, showing improved error control and power in simulations and real data.