bunnech / cellotLinks
Learning Single-Cell Perturbation Responses using Neural Optimal Transport
☆156Updated last year
Alternatives and similar repositories for cellot
Users that are interested in cellot are comparing it to the libraries listed below
Sorting:
- Modeling complex perturbations with CellFlow☆114Updated last week
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆134Updated last year
- Models and datasets for perturbational single-cell omics☆172Updated 3 years ago
- A model developed for the generation of scRNA-seq data☆84Updated 2 weeks ago
- ☆66Updated 2 years ago
- Comprehensive suite for evaluating perturbation prediction models☆120Updated 2 weeks ago
- Repository for Nicheformer: a foundation model for single-cell and spatial omics☆149Updated 2 months ago
- ☆90Updated last year
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆164Updated 11 months ago
- Evaluation suite for transcriptomic perturbation effect prediction models. Includes support for single-cell foundation models.☆35Updated 6 months ago
- ☆69Updated 6 months ago
- Multi-omic single-cell optimal transport tools☆185Updated last week
- Single-Cell (Perturbation) Model Library☆89Updated 2 weeks ago
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆142Updated 11 months ago
- UCE is a zero-shot foundation model for single-cell gene expression data☆238Updated 11 months ago
- Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.☆101Updated last year
- Single-cell perturbation analysis☆281Updated this week
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆61Updated 3 weeks ago
- Learning cell communication from spatial graphs of cells☆114Updated 2 years ago
- ☆38Updated last month
- ☆18Updated 4 years ago
- 🏃 The go-to single-cell Foundation Model☆135Updated last month
- A simulator for single-cell expression data guided by gene regulatory networks☆73Updated last year
- Single-cell perturbation effects prediction benchmark☆58Updated 3 weeks ago
- BEELINE: evaluation of algorithms for gene regulatory network inference☆204Updated 2 months ago
- Gromov-Wasserstein based optimal transport for aligning single-cell multi-omics data☆78Updated 2 years ago
- ☆79Updated 3 weeks ago
- GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations☆331Updated last year
- A unified approach for integrating spatial and single-cell transcriptomics data by leveraging deep generative models☆77Updated last year
- Accelerated, Python-only, single-cell integration benchmarking metrics☆85Updated last week