Viivi Halla-aho

The paper evaluates statistical and probabilistic methods for classifying cancer types using cell-free DNA methylation data, finding that some methods outperform GLMnet and show varying performance across sequencing depths and cancer types.

The paper evaluates the type 1 error rate in bisulfite sequencing analysis of umbilical cord blood samples and finds that permutation-based significance analysis reduces it, with minimal differential methylation associated with clinical variables except sex.

LuxUS improves differential DNA methylation analysis by incorporating spatial correlation and mixed models, enhancing statistical power and detecting biologically significant cytosines.

LuxHS improves DNA methylation analysis by incorporating a sparsity promoting prior to account for deviations from spatial correlation patterns in BS-seq data.

LuxRep improves methylation level and differentially methylated site estimation by incorporating low bisulfite conversion rate technical replicates in BS-seq analysis.