Katharina Jahn

Probabilistic cell lineage inference from mitochondrial mutations

mt-SCITE reconstructs cell lineages from mitochondrial mutations, outperforming existing methods and revealing subclonal relationships in CD8+ T cells post-vaccination.

Joint copy number and mutation phylogeny reconstruction fromsingle-cell amplicon sequencing data

This paper introduces COMPASS, a method to jointly reconstruct mutation and copy number phylogenies from single-cell amplicon sequencing data, showing it detects both clonal and subclonal alterations in leukemia patients.

scROSHI - robust supervised hierarchical identification of single cells

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.

The Bourque Distances for Mutation Trees of Cancers

The paper introduces Bourque distances to compare mutation trees in cancer, addressing limitations of existing metrics like Robinson-Foulds distance.

Within-patient genetic diversity of SARS-CoV-2

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

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.

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

This paper develops a computational method to infer tumour evolution trees from combined single-cell and bulk sequencing data, showing improved accuracy in subclone identification and mutation history reconstruction.

SCIΦ: Single-cell mutation identification via phylogenetic inference

SCIΦ identifies mutations in single-cell tumor data by inferring phylogenetic relationships, outperforming existing methods on simulated and real datasets.

Tree inference for single-cell data

SCITE is a stochastic algorithm for inferring tumor mutation histories from single-cell data, showing improved accuracy and scalability.