Catalina A. Vallejos

The paper benchmarks tools for locus-specific TE analysis in scRNA-seq, finding older TEs are more accurately quantified, while young TEs face challenges due to multi-mapping reads.

The paper shows that imputing DNA methylation loci from epigenome-wide data provides stronger associations with smoking and age than directly measured CpGs.

This paper develops an epigenetic score to objectively measure alcohol consumption using blood-based DNA methylation and validates it across multiple cohorts, finding associations with brain health metrics.

This paper introduces RTFS, a method for pre-selecting CpGs for disease prediction models, and shows it performs well for predicting type 2 diabetes onset risk.

The paper identifies inter-individual variability in neuronal heat shock protein gene expression as a predictor of stress survival in C. elegans.

The paper identifies regional glial heterogeneity in human white matter using single nucleus RNA sequencing, revealing tissue-specific glial populations and age/sex-related gene expression differences.

The paper evaluates normalization methods for DNA methylation data to improve BMI prediction across cohorts when projecting new data onto a reference panel.

The paper refines epigenetic predictors of chronological and biological age using large-scale EWAS and external validation, improving cAge prediction and outperforming existing bAge predictors.

SCRaPL improves statistical power in single-cell multi-omics correlation analysis by modeling experimental noise, achieving higher sensitivity with similar false positive rates.

The paper develops a single-nucleus RNA-seq2 method to study ploidy and spatial zonation in hepatocytes, finding gene expression in tetraploid cells depends on their hepatic lobe position.

scMET quantifies DNA methylation heterogeneity at single-cell resolution using a Bayesian model, identifying variable features and enabling differential analysis between cell groups.

The paper introduces a method to measure expression variability in single-cell RNA sequencing data, revealing synchronized translational machinery activity in immune cells and new patterns in CD4+ T cell differentiation.