Paul Villoutreix

Kontext identifies spatial domains and cell-cell communication by integrating ligand-receptor interactions, outperforming existing methods in spatial transcriptomics.

The paper introduces minkiPy, a geometric framework for analyzing spatial gene expression patterns using Minkowski functionals and tensors, and applies it to spatial transcriptomics datasets.

jsPCA identifies spatial domains and variable genes in spatial transcriptomics data efficiently and accurately, outperforming existing methods in speed and scalability.

The paper quantifies the contributions of mosaic and regulative developmental modes in C. elegans using single-cell trajectories and spatial gene expression data.

MOTL improves multi-omics data factorization for small datasets by transferring knowledge from large datasets, enhancing cancer subtype identification in glioblastoma samples.

Spatial-scERA reconstructs spatial single-cell enhancer activity in multicellular organisms using integrated transcriptomic and spatial data.

The paper analyzes growth plate architecture in mouse embryos and finds that chondrocyte columns are not formed during embryonic bone elongation, suggesting different cellular mechanisms in pre- and postnatal development.

The paper introduces 3D MAPs, a pipeline for analyzing chondrocyte morphology in growth plates, revealing allometric growth strategies and GDF5's regulatory role in tibia development.

The paper analyzes 3D nuclear morphology in mouse growth plates to reveal differences in nuclear shape across differentiation stages and opposing growth plates.

The paper develops a semi-supervised learning framework to integrate heterogeneous biological data and demonstrates its application in analyzing Drosophila pattern formation.