P
Pierre Boyeau
Researcher at University of California, Berkeley
Publications - 11
Citations - 260
Pierre Boyeau is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 4, co-authored 5 publications receiving 37 citations. Previous affiliations of Pierre Boyeau include École Normale Supérieure & École des ponts ParisTech.
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Journal ArticleDOI
A Python library for probabilistic analysis of single-cell omics data
Adam Gayoso,Romain Lopez,Galen Xing,Pierre Boyeau,Valeh Valiollah Pour Amiri,Justin Hong,Katherine Wu,Michael Jayasuriya,Edouard Mehlman,Maxime Langevin,Yining Liu,Jules Samaran,Gabriel Misrachi,Achille Nazaret,Oscar Clivio,Chenling Xu,Tal Ashuach,Mariano I. Gabitto,Mohammad Lotfollahi,Valentine Svensson,Eduardo da Veiga Beltrame,Vitalii Kleshchevnikov,Carlos Talavera-López,Lior Pachter,Fabian J. Theis,Aaron M. Streets,Michael I. Jordan,Jeffrey Regier,Nir Yosef +28 more
Posted ContentDOI
scvi-tools: a library for deep probabilistic analysis of single-cell omics data
Adam Gayoso,Romain Lopez,Galen Xing,Pierre Boyeau,Pierre Boyeau,Kenneth Wu,Jayasuriya M,Melhman E,Melhman E,Maxime Langevin,Yuzhong Liu,Jules Samaran,Misrachi G,Nazaret A,Clivio O,Chenling Xu,Tal Ashuach,Mohammad Lotfollahi,Svensson,Beltrame EdV,Carlos Talavera-López,Carlos Talavera-López,Lior Pachter,Fabian J. Theis,Aaron M. Streets,Michael I. Jordan,Jeffrey Regier,Nir Yosef +27 more
TL;DR: Scvi-tools as mentioned in this paper is a Python package that implements a variety of leading probabilistic methods for single-cell omics data analysis, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities.
Posted ContentDOI
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation
Romain Lopez,Baoguo Li,Hadas Keren-Shaul,Pierre Boyeau,Kedmi M,David Pilzer,Adam Jelinski,Eyal David,Allon Wagner,Addad Y,Michael I. Jordan,Michael I. Jordan,Ido Amit,Nir Yosef +13 more
TL;DR: Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI) as mentioned in this paper is a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types.
Posted Content
Decision-Making with Auto-Encoding Variational Bayes
TL;DR: This work describes the error of importance sampling as a function of posterior variance and shows that proposal distributions learned with evidence upper bounds are better than the current state of the art.
Posted ContentDOI
Deep Generative Models for Detecting Differential Expression in Single Cells
Pierre Boyeau,Pierre Boyeau,Romain Lopez,Jeffrey Regier,Adam Gayoso,Michael I. Jordan,Nir Yosef +6 more
TL;DR: This work shows that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells and introduces a technique for improving the posterior approximation, which improves differential expression performance.