Ulrike Menzel

scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

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

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.

gExcite - A start-to-end framework for single-cell gene expression, hashing, and antibody analysis

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: 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.

Learning The High-Dimensional Immunogenomic Features That Predict Public And Private Antibody Repertoires

The paper identifies high-dimensional immunogenomic features that predict public and private antibody clones in humans and mice with 80% accuracy using machine learning.