Gryte Satas

The paper shows that error-correction objectives for perfect phylogenies are statistically inconsistent, often favoring incorrect trees over the true one in simulated data.

scPlOver infers DNA content from single-cell WGS data using fragment overlaps, improving accuracy in identifying high DNA content cells in ovarian cancer.

This study tracks clonal evolution of drug resistance in ovarian cancer using structural variants in cfDNA, finding polyclonal resistance with dominant high-fitness clones and genomic features linked to drug resistance.

This study shows that ongoing whole-genome doubling in ovarian cancer promotes cell diversity and immune dysregulation through chromosomal instability and quiescent states.

This paper introduces ArtiCull, a method to analyze single-cell whole-genome sequencing data to distinguish active from historical mutational processes in cancer, revealing temporal and spatial patterns in mutational signatures.

This paper introduces DeCiFer, a method to more accurately estimate cancer cell fractions and infer tumor evolution by addressing limitations of existing methods in the presence of copy-number aberrations and mutation losses.

This paper introduces a new method for inferring tumor phylogenies from single-cell sequencing data that accounts for copy-number aberrations, improving accuracy in tumors with such aberrations.

This paper introduces CALDER, an algorithm that infers tumor phylogenies from longitudinal bulk DNA sequencing data, showing improved accuracy over existing methods.