theislab / 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.
☆88Updated 3 months ago
Related projects ⓘ
Alternatives and complementary repositories for cpa
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆48Updated 2 years ago
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆104Updated 3 months ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆109Updated 2 months ago
- Models and datasets for perturbational single-cell omics☆143Updated 2 years ago
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆29Updated this week
- ☆56Updated last year
- Additional code and analysis from the single-cell integration benchmarking project☆55Updated last year
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.☆51Updated 2 months ago
- A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to repr…☆93Updated this week
- ☆16Updated 3 years ago
- ☆68Updated 3 months ago
- DIALOGUE is a dimensionality reduction method that uses cross-cell-type associations to identify multicellular programs (MCPs) and map th…☆105Updated 9 months ago
- ☆72Updated last year
- Perturbation Analysis in the scverse ecosystem.☆141Updated 3 weeks ago
- Single-Cell (Perturbation) Model Library☆22Updated last week
- Accelerated, Python-only, single-cell integration benchmarking metrics☆49Updated this week
- Large language modeling applied to T-cell receptor (TCR) sequences.☆47Updated 2 years ago
- repository containing analysis scripts and auxiliary files☆30Updated 4 years ago
- Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data☆113Updated last year
- ☆97Updated last year
- Diffusion model for gene regulatory network inference.☆15Updated 5 months ago
- ☆108Updated 4 months ago
- Computational Optimization of DNA Activity (CODA)☆39Updated 2 months ago
- SIMBA: SIngle-cell eMBedding Along with features☆55Updated last month
- spatial transcriptome, single cell☆65Updated last year
- This repository implements Graph Variational Causal Inference (graphVCI), a framework that integrates prior knowledge of relational infor…☆15Updated last year
- Quantifying experimental perturbations at single cell resolution☆105Updated last month
- High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations☆47Updated last year
- Learning Single-Cell Perturbation Responses using Neural Optimal Transport☆118Updated 3 weeks ago
- single cell foundation model for Gene network inference and more☆24Updated this week