Rosemary Braun

This study evaluates copula models for gene coexpression in scRNA-seq data, finding Gaussian copulas offer the best balance of accuracy and speed.

The paper introduces a method to identify distinct topological structures in high-dimensional single-cell RNA sequencing data to disentangle biological processes.

This paper benchmarks circadian detection methods on single-cell RNAseq data and proposes a subsampling and harmonic regression strategy for detecting circadian genes.

This paper introduces SNOW, a deep learning method for analyzing single-cell time series data, showing it can separate time-dependent and independent components and remove batch effects.

The paper analyzes bulk RNA sequencing data from Drosophila fat bodies under different temperatures to show temperature-dependent circadian transcriptome changes and gene coordination.

This paper introduces scHolography, a deep learning method to reconstruct 3D tissue organization from single-cell transcriptome data, enabling analysis of cell-cell interactions and spatial heterogeneity.

This study shows that dietary restriction in Drosophila affects diurnal rhythms and longevity through clock-controlled proteasome subunits.

The paper quantitatively analyzes transcriptome dynamics during Xenopus embryogenesis to understand how pluripotent cells transition to lineage-restricted states.

This study identifies translation factors that mediate temperature compensation in the circadian clock by altering the temperature dependence of PER protein oscillations.

TimeCycle detects cycling transcripts in circadian time-series data using topology-based methods, showing effectiveness across various sampling schemes and data conditions.

TimeTrial evaluates cycle detection algorithms in circadian transcriptomics by analyzing real and synthetic data to optimize experimental design and analysis choices.

This paper develops a Drosophila model for TBI and identifies glial immune pathways as mediators of TBI effects on behavior.