Paul Villoutreix
@paulvilloutreix.bsky.social
We develop machine learning frameworks to decode the geometry and topology of tissue development from spatial omics data | Junior Professor INSERM & Group Leader Turing Center for Living Systems | https://bioml.lis-lab.fr
... and the application process: centuri-livingsystems.org/centuri-phd-...
If you want to develop new quantitative methods, collaborate across disciplines, and work on an exciting biological problem, we’d love to hear from you!
If you want to develop new quantitative methods, collaborate across disciplines, and work on an exciting biological problem, we’d love to hear from you!
centuri-livingsystems.org
November 15, 2025 at 3:42 PM
... and the application process: centuri-livingsystems.org/centuri-phd-...
If you want to develop new quantitative methods, collaborate across disciplines, and work on an exciting biological problem, we’d love to hear from you!
If you want to develop new quantitative methods, collaborate across disciplines, and work on an exciting biological problem, we’d love to hear from you!
You will join the collaborative, interdisciplinary environment of the Turing Centre for Living Systems (CENTURI) in Marseille, working within the teams of Paul Villoutreix and Robert Kelly!
The full project: centuri-livingsystems.org/wp-content/u...
The full project: centuri-livingsystems.org/wp-content/u...
November 15, 2025 at 3:42 PM
You will join the collaborative, interdisciplinary environment of the Turing Centre for Living Systems (CENTURI) in Marseille, working within the teams of Paul Villoutreix and Robert Kelly!
The full project: centuri-livingsystems.org/wp-content/u...
The full project: centuri-livingsystems.org/wp-content/u...
Congrats Baptiste on the PhD and a beautiful paper !
academic.oup.com/nar/article/...
academic.oup.com/nar/article/...
Spatial-scERA: a method for reconstructing spatial single-cell enhancer activity in multicellular organisms
Abstract. Enhancers play an essential role in developmental processes by regulating the spatiotemporal expression of genes. Characterizing their spatiotemp
academic.oup.com
November 10, 2025 at 11:57 AM
Congrats Baptiste on the PhD and a beautiful paper !
academic.oup.com/nar/article/...
academic.oup.com/nar/article/...
Reposted by Paul Villoutreix
Very elegant and efficient methodology to recover spatial patterns from multi-modal spatial datasets 👌!! Many congratulations @paulvilloutreix.bsky.social and to all authors👏😀!!
September 19, 2025 at 9:16 AM
Very elegant and efficient methodology to recover spatial patterns from multi-modal spatial datasets 👌!! Many congratulations @paulvilloutreix.bsky.social and to all authors👏😀!!
Link to the preprint ->
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data
Spatially structured cell heterogeneity within tissues is essential for healthy organ function. This heterogeneity is reflected by differential gene expression activity at various spatial location. Sp...
www.biorxiv.org
September 19, 2025 at 6:51 AM
Link to the preprint ->
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
jsPCA is simple, it is based on the product between the gene expression covariance (classical PCA) and the spatial autocorrelation.
We have shown that it's fast, interpretable and highly adaptable to multiple settings and large datasets.
Congrats to Ines Assali who led the project!
We have shown that it's fast, interpretable and highly adaptable to multiple settings and large datasets.
Congrats to Ines Assali who led the project!
September 19, 2025 at 6:50 AM
jsPCA is simple, it is based on the product between the gene expression covariance (classical PCA) and the spatial autocorrelation.
We have shown that it's fast, interpretable and highly adaptable to multiple settings and large datasets.
Congrats to Ines Assali who led the project!
We have shown that it's fast, interpretable and highly adaptable to multiple settings and large datasets.
Congrats to Ines Assali who led the project!
We came up with an initial formulation of jsPCA last year when studying single cell morphometrics data
www.nature.com/articles/s41...
which was build on sPCA www.nature.com/articles/hdy....
To adapt it to spatial transcriptomics, we had to leverage sparsity and non convex optimization on manifold.
www.nature.com/articles/s41...
which was build on sPCA www.nature.com/articles/hdy....
To adapt it to spatial transcriptomics, we had to leverage sparsity and non convex optimization on manifold.
Single-cell morphometrics reveals T-box gene-dependent patterns of epithelial tension in the Second Heart field - Nature Communications
The embryonic heart tube undergoes elongation via the addition of progenitors from the second heart field, though how epithelial mechanics and genetics interact during this process remains unknown. He...
www.nature.com
September 19, 2025 at 6:50 AM
We came up with an initial formulation of jsPCA last year when studying single cell morphometrics data
www.nature.com/articles/s41...
which was build on sPCA www.nature.com/articles/hdy....
To adapt it to spatial transcriptomics, we had to leverage sparsity and non convex optimization on manifold.
www.nature.com/articles/s41...
which was build on sPCA www.nature.com/articles/hdy....
To adapt it to spatial transcriptomics, we had to leverage sparsity and non convex optimization on manifold.
Finally, principal components of jsPCA are directly interpretable in terms of spatially variable genes (SVG). We found that the top 3000 genes of the first principal component recovered 80% of the SVGs obtained by the widely used SPARK-X method.
September 19, 2025 at 6:50 AM
Finally, principal components of jsPCA are directly interpretable in terms of spatially variable genes (SVG). We found that the top 3000 genes of the first principal component recovered 80% of the SVGs obtained by the widely used SPARK-X method.
jsPCA can also learn a joint representations on some of the slices and use it to predict the domains in an unseen slice.
September 19, 2025 at 6:50 AM
jsPCA can also learn a joint representations on some of the slices and use it to predict the domains in an unseen slice.
The joint analysis of multiple slices by jsPCA generates common domains among datasets, in contrast to monoslice analysis..
September 19, 2025 at 6:50 AM
The joint analysis of multiple slices by jsPCA generates common domains among datasets, in contrast to monoslice analysis..
jsPCA achieves state-of-the-art accuracy for domain identification while reducing computation from hours to seconds, and scales to atlas-level datasets such as Stereo-seq.
September 19, 2025 at 6:50 AM
jsPCA achieves state-of-the-art accuracy for domain identification while reducing computation from hours to seconds, and scales to atlas-level datasets such as Stereo-seq.