Pierre Bost

This paper introduces TranspaceR, an R package with statistical methods for analyzing spatial transcriptomics data, improving quality control, gene selection, and normalization for large-scale datasets.

This paper introduces PCF-SiM, a framework for quantifying spatial organization in multiplexed imaging data, demonstrating its application in detecting tissue remodeling and spatial interactions in disease models.

This paper introduces a method for high-plex protein imaging using MALDI-IHC with PCMT-labeled antibodies and presents an open-source analysis pipeline called gutenTAG.

The paper develops statistical models for multiplexed imaging data to improve differential abundance testing and proposes a sampling strategy to enhance statistical power.

The paper develops a statistical framework to estimate tissue spatial segregation, enabling optimal design of multiplexed imaging experiments by determining the necessary fields of view to capture all cell types.