facebookresearch / CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
☆180Updated last year
Alternatives and similar repositories for CPA
Users that are interested in CPA are comparing it to the libraries listed below
Sorting:
- Learning Single-Cell Perturbation Responses using Neural Optimal Transport☆132Updated 6 months ago
- Models and datasets for perturbational single-cell omics☆154Updated 2 years ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆100Updated 9 months ago
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆112Updated 3 months ago
- Single cell perturbation prediction☆293Updated 5 months ago
- GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations☆254Updated 3 months ago
- Formalizing and benchmarking open problems in single-cell genomics☆332Updated 3 weeks ago
- Reference mapping for single-cell genomics☆363Updated 2 months ago
- Deep learning based processing of Atac-seq data☆129Updated 2 years ago
- Perturbation Analysis in the scverse ecosystem.☆173Updated this week
- ☆78Updated 9 months ago
- Discovering novel cell types across heterogenous single-cell experiments☆123Updated 2 years ago
- Multi-omic single-cell optimal transport tools☆152Updated last week
- Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks☆83Updated 11 months ago
- ☆19Updated 2 years ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆124Updated 2 months ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆50Updated 3 years ago
- A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to repr…☆188Updated this week
- ☆61Updated last year
- Biological Network Integration using Convolutions☆62Updated last year
- Benchmarking analysis of data integration tools☆348Updated 4 months ago
- Images and other data from the JUMP Cell Painting Consortium☆168Updated 2 months ago
- Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction …☆449Updated 4 months ago
- Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors☆40Updated 11 months ago
- Learning cell communication from spatial graphs of cells☆110Updated last year
- High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations☆48Updated 3 months ago
- Training and evaluating a variational autoencoder for pan-cancer gene expression data☆169Updated 6 years ago
- Accelerated, Python-only, single-cell integration benchmarking metrics☆58Updated this week
- gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.☆263Updated 3 weeks ago
- ☆62Updated 8 months ago