Article

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

2018-11-29

Abstract excerpt

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3193b42e-70cd-5063-bfa5-ea850a628fa0
DOI: 10.1101/478503

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and speciesDOI 10.1101/478503Read

TRAILBLAZER: generative multicellular perturbation model of biologySemantic proximity Read

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseasesSemantic proximity Read

Squidiff: Predicting cellular development and responses to perturbations using a diffusion modelSemantic proximity Read

Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding spaceSemantic proximity Read

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spacesSemantic proximity Read

Parameter-free representations outperform single-cell foundation models on downstream benchmarksSemantic proximity Read

AetherCell: A generative engine for virtual cell perturbation and <i>in vivo</i> drug discoverySemantic proximity Read

CellPulse: A Foundation Model of Coordinated Gene Dynamics Simulating Viral Infectious DiseasesSemantic proximity Read

Designing Single Cell RNA-Sequencing Experiments for Learning Latent RepresentationsSemantic proximity Read

PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect PredictionSemantic proximity Read

scGFT: single-cell RNA-seq data augmentation using generative Fourier transformerSemantic proximity Read

Learning Genetic Perturbation Effects with Variational Causal InferenceSemantic proximity Read