Franziska Singer

This paper introduces scClone2DR, a framework predicting tumour drug response at the clone level using single-cell DNA/RNA sequencing and drug-screening data, showing improved accuracy in identifying clonal sensitivities and resistance.

The paper characterizes respiratory virus genomic diversity in Switzerland using hybrid-capture sequencing and phylogenetic analysis, identifying prevalent strains and evolutionary insights.

V-pipe 3.0 is a computational pipeline for analyzing viral genomic data, enabling reproducible and scalable estimation of within-sample genetic diversity.

scAmpi provides a clinical workflow for scRNA-seq analysis, extracting clinically relevant information and supporting drug treatment identification.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

gExcite is a start-to-end framework for analyzing single-cell gene expression and CITE-seq data, including hashing deconvolution, and is scalable for multiple samples.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper presents an automated workflow for in-silico drug response prediction in bladder cancer using multi-omic profiles, identifying personalized treatment options for most patients.