Harald S. Vöhringer

The paper introduces spatialproteomics, a Python toolbox for analyzing highly multiplexed fluorescence images, demonstrating its use on lymph node and lymphoma data.

This paper introduces scPCA, a dimensionality reduction framework that integrates cellular heterogeneity and condition effects in scRNA-seq data, revealing transcriptional changes across conditions and cell types.

SOFA identifies novel sources of variation in multi-omic data by incorporating known covariates and focusing on unexplained factors.

SpatialData, a Zarr/OME-NGFF-based format plus Python library, unifies images, labels, points, shapes and tables across spatial omics platforms with coordinate alignment, lazy loading and cross-modal aggregation.

This study reveals that cellular differentiation drives intratumor heterogeneity in B-cell lymphomas, with distinct maturation states associated with different survival outcomes.

The paper quantifies mismatch repair (MMR) activity in human tissues by analyzing mutation rates in individuals with constitutional MMR deficiency, revealing genotype-specific mutational signatures and the role of MMR components in DNA repair.

This paper uses deep transfer learning on H&E images to correlate histopathological patterns with genomic and survival data across 28 cancer types, revealing associations between histopathology and molecular features.

This study shows that mutation spectra in C. elegans are shaped by both DNA damage and repair processes, with repair deficiencies altering mutation rates and spectra in many experimental conditions.

The paper introduces HH-suite3, an accelerated version of HH-suite for faster remote homology detection and protein annotation, with significant speed improvements over previous versions and other tools.