Florian Wagner

Monet is an open-source Python package that provides PCA-based latent space analysis and integration tools for scRNA-Seq data, supporting tasks like batch correction and label transfer.

This paper introduces Galapagos, a clustering workflow for scRNA-seq data using t-SNE and DBSCAN, showing it effectively identifies cell types and is robust to noise and parameter choices.

This paper introduces ENHANCE, a denoising method for single-cell RNA-Seq data using PCA with a cell aggregation step, showing improved accuracy over existing methods.

This study integrates single-cell RNA-Seq and spatial transcriptomics data from pancreatic cancer tissue to map cell type spatial distributions and infer tissue architecture.

Chained linear SVMs on kNN-smoothed, PCA-transformed scRNA-seq classify closely related PBMC subtypes across 10x chemistries, plus a 'mirror validation' check that estimates accuracy without ground truth.

Aggregating UMI counts from a cell's nearest neighbours, chosen step-wise on variance-stabilised profiles, cuts technical noise enough that PCA and hierarchical clustering resolve cell types and T cell subsets.