theislab / cpaLinks
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.
☆112Updated last year
Alternatives and similar repositories for cpa
Users that are interested in cpa are comparing it to the libraries listed below
Sorting:
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆49Updated 3 months ago
- ☆54Updated 2 months ago
- Models and datasets for perturbational single-cell omics☆164Updated 3 years ago
- ☆84Updated last year
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆148Updated 7 months ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆53Updated 3 years ago
- Modeling complex perturbations with CellFlow☆87Updated last week
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆124Updated 8 months ago
- ☆35Updated 2 weeks ago
- Comprehensive suite for evaluating perturbation prediction models☆89Updated last week
- Evaluation suite for transcriptomic perturbation effect prediction models. Includes support for single-cell foundation models.☆26Updated 2 months ago
- Code for evaluating single cell foundation models scBERT and scGPT☆46Updated last year
- Diffusion model for gene regulatory network inference.☆24Updated 2 months ago
- ☆28Updated 5 years ago
- 🏃 The go-to single-cell Foundation Model☆108Updated this week
- ☆18Updated 4 years ago
- Additional code and analysis from the single-cell integration benchmarking project☆67Updated 2 years ago
- ☆63Updated 2 years ago
- SIMBA: SIngle-cell eMBedding Along with features☆63Updated 11 months ago
- Unsupervised Deep Disentangled Representation of Single-Cell Omics☆51Updated last week
- ☆85Updated 2 years ago
- Single-Cell (Perturbation) Model Library☆75Updated last month
- repository containing analysis scripts and auxiliary files☆36Updated 5 years ago
- Learning Single-Cell Perturbation Responses using Neural Optimal Transport☆146Updated 11 months ago
- Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information (ICLR 2023)☆19Updated 3 months ago
- ☆139Updated last year
- A simulator for single cell multi-omics and spatial omics data that provides ground truth to benchmark a wide range of methods.☆61Updated 2 weeks ago
- Transformer for One-Stop Interpretable Cell-type Annotation☆145Updated last year
- Accelerated, Python-only, single-cell integration benchmarking metrics☆72Updated this week
- ☆72Updated last year