Maren Büttner

ConvexGating automatically learns unbiased, interpretable gating strategies for flow cytometry data, improving consistency and reducing contamination in cell population identification.

Loss of extracellular matrix-integrin signaling switches off YAP/TEAD and lets prostate adenocarcinoma organoids turn neuroendocrine; LATS inhibition reverses it, and combined YAP, NOTCH and AR inhibition fully reprograms them.

The paper integrates single-cell multiomics and CRISPR screening in prostate cancer organoids to reveal lineage-specific dependencies and heterogeneity effects on gene essentiality.

This study uses longitudinal scRNA-seq data from a mouse colitis model to build ODE-based models of immune dynamics, validating predictions with flow cytometry and human IBD gene expression data.

This paper integrates multiple scRNA-seq datasets to create a mouse islet atlas, revealing new insights into β-cell states and differences across diabetes models.

Pytometry provides a Python framework for analyzing flow and mass cytometry data using tools adapted from single-cell genomics analysis, enabling automated preprocessing, cell type annotation, and disease classification.

BBKNN, Scanorama, and scVI lead complex scRNA-seq integration in a 38-setup, nine-task, 14-metric benchmark; highly variable gene selection helps, whereas scaling favors batch removal over bio-conservation.

scCODA is a Bayesian model for analyzing compositional single-cell data, improving detection of cell type changes in disease and other stimuli.

This study evaluates 10 classification methods across 14 datasets and finds that deep learning does not outperform classical machine learning for cell-type annotation.

The paper identifies Wnt/PCP-activated Lgr5+ intestinal stem cells that directly differentiate into enteroendocrine or Paneth cells, challenging previous models of multipotency.

This paper introduces scArches, a deep learning method for mapping query datasets onto reference single-cell atlases using transfer learning, effectively reducing batch effects and preserving biological information with fewer parameters.

This paper introduces kBET, a new metric to assess batch effects in scRNA-seq data and evaluates its effectiveness compared to existing methods.