Katharina Schmid

XplainTDA identifies molecular determinants of topological structures in single-cell multi-omics data through perturbation-based analysis.

This study benchmarks six scRNA-seq CNV callers using 15 datasets, finding that reference choice and allelic information impact performance, with some methods being more robust but computationally intensive.

The paper develops and validates multiplexing strategies for brain organoids combined with single cell RNA-seq to study neurodevelopmental trajectories across multiple genetic lines.

The paper identifies co-expression QTLs (co-eQTLs) using single-cell RNA-seq data, revealing how genetic variants affect gene co-expression patterns and regulatory networks in specific cell types.

The paper presents a statistical framework for experimental design and power analysis in single cell RNA-seq studies, showing that higher power is achieved with shallow sequencing of more cells per individual.