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.
☆107Updated 11 months ago
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☆40Updated last month
- Models and datasets for perturbational single-cell omics☆157Updated 2 years ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆131Updated 5 months ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆50Updated 3 years ago
- ☆34Updated 3 weeks ago
- Modeling complex perturbations with CellFlow☆76Updated this week
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.☆71Updated last month
- ☆80Updated 11 months ago
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆121Updated 6 months ago
- Single-Cell (Perturbation) Model Library☆64Updated last week
- Code for evaluating single cell foundation models scBERT and scGPT☆46Updated 11 months ago
- ☆132Updated last year
- Evaluation suite for transcriptomic perturbation effect prediction models. Includes support for single-cell foundation models.☆27Updated 2 weeks ago
- Additional code and analysis from the single-cell integration benchmarking project☆67Updated 2 years ago
- ☆63Updated 2 years ago
- Learning Single-Cell Perturbation Responses using Neural Optimal Transport☆142Updated 9 months ago
- Single-cell biological network inference using a heterogeneous graph transformer☆73Updated 5 months ago
- ☆84Updated 2 years ago
- ☆35Updated 3 months ago
- ☆34Updated 2 months ago
- ☆17Updated 4 years ago
- Repository for Nicheformer: a foundation model for single-cell and spatial omics☆95Updated 4 months ago
- High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations☆50Updated 6 months ago
- single cell foundation model for Gene network inference and more☆97Updated this week
- A model developed for the generation of scRNA-seq data☆68Updated 5 months ago
- SIMBA: SIngle-cell eMBedding Along with features☆63Updated 9 months ago
- ☆36Updated 2 months ago
- Accelerated, Python-only, single-cell integration benchmarking metrics☆63Updated this week
- ☆32Updated 3 months ago
- A simulator for single cell multi-omics and spatial omics data that provides ground truth to benchmark a wide range of methods.☆57Updated 7 months ago