Search Results for author: Aviv Regev

Found 9 papers, 4 papers with code

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport

no code implementations1 May 2024 Jayoung Ryu, Romain Lopez, Charlotte Bunne, Aviv Regev

It is now possible to conduct large scale perturbation screens with complex readout modalities, such as different molecular profiles or high content cell images.

Toward the Identifiability of Comparative Deep Generative Models

no code implementations29 Jan 2024 Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali, Jonathan Pritchard, Aviv Regev

Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization.

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

1 code implementation7 Nov 2022 Romain Lopez, Nataša Tagasovska, Stephen Ra, Kyunghyn Cho, Jonathan K. Pritchard, Aviv Regev

Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.

Disentanglement Domain Generalization +1

Large-Scale Differentiable Causal Discovery of Factor Graphs

1 code implementation15 Jun 2022 Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev

Combining this novel structural assumption with recent advances that bridge the gap between causal discovery and continuous optimization, we achieve causal discovery on thousands of variables.

Causal Discovery Causal Inference

Cell types and ontologies of the Human Cell Atlas

no code implementations28 Jun 2021 David Osumi-Sutherland, Chuan Xu, Maria Keays, Peter V. Kharchenko, Aviv Regev, Ed Lein, Sarah A. Teichmann

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge.

Inference of cell dynamics on perturbation data using adjoint sensitivity

1 code implementation13 Apr 2021 Weiqi Ji, Bo Yuan, Ciyue Shen, Aviv Regev, Chris Sander, Sili Deng

While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability.

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data

no code implementations28 Nov 2018 Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick

Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.

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