Christophe Vanderaa

gamdid is a statistical framework for differential distribution analysis in single-cell proteomics, detecting shape differences and outperforming existing methods for such cases.

scplainer provides a standardized approach using linear models to analyze single-cell proteomics data, enabling variance analysis, differential abundance, and integration across experiments.

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

omicsGMF integrates dimensionality reduction, batch correction, and imputation for bulk and single-cell proteomics data, improving statistical power for differential abundance detection.

The paper provides a Bioconductor package with curated landmark datasets for single-cell multimodal data, enabling easy access and analysis of seven different technologies.

The paper identifies tissue-enriched ligands involved in epithelial-endothelial crosstalk during pancreas development using scRNA-seq and computational interactomics.

The paper replicates a landmark single-cell proteomics study to highlight computational challenges in data analysis and proposes a standardized workflow.