yulun-rayn / graphVCI
This repository implements Graph Variational Causal Inference (graphVCI), a framework that integrates prior knowledge of relational information into variational causal inference for the prediction of perturbation effect on gene expressions at single-cell and marginal level.
☆17Updated 2 months ago
Alternatives and similar repositories for graphVCI:
Users that are interested in graphVCI are comparing it to the libraries listed below
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆49Updated 3 years ago
- ☆17Updated 3 years ago
- ☆54Updated last year
- Deep learning model for single-cell inference of multi-omic profiles from a single input modality.☆41Updated last year
- Diffusion model for gene regulatory network inference.☆18Updated 3 weeks ago
- ☆25Updated 4 years ago
- scDiff: A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models☆27Updated 8 months ago
- ☆14Updated 2 years ago
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆32Updated this week
- Additional code and analysis from the single-cell integration benchmarking project☆60Updated 2 years ago
- A simulator for single-cell expression data guided by gene regulatory networks☆61Updated 11 months ago
- repository containing analysis scripts and auxiliary files☆33Updated 5 years ago
- ☆25Updated 3 months ago
- ☆60Updated last year
- resVAE is a restricted latent variational autoencoder that we wrote to uncover hidden structures in gene expression data, especially usin…☆12Updated 2 years ago
- ☆58Updated 7 months ago
- The software of Pamona, a partial manifold alignment algorithm.☆19Updated 4 years ago
- ☆12Updated 3 weeks ago
- ☆77Updated last year
- ☆31Updated last week
- ☆20Updated 6 months ago
- ACTIONet single-cell analysis framework☆38Updated 7 months ago
- Single cell joint embedding and modality prediction with autoencoder☆9Updated 2 years ago
- A simulator for single cell multi-omics and spatial omics data that provides ground truth to benchmark a wide range of methods.☆33Updated 3 months ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆99Updated 8 months ago
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.☆68Updated 3 months ago
- ☆45Updated 2 years ago
- Single-Cell (Perturbation) Model Library☆51Updated last month
- Functional and Learnable Cell dynamicS☆15Updated last month
- ☆16Updated 2 years ago