PayamDiba / SERGIO
A simulator for single-cell expression data guided by gene regulatory networks
β59Updated 10 months ago
Alternatives and similar repositories for SERGIO:
Users that are interested in SERGIO are comparing it to the libraries listed below
- Git Repo for simulating Boolean Modelsβ33Updated 9 months ago
- β58Updated last year
- Simulating single-cell data using gene regulatory networks πβ74Updated last year
- Models and datasets for perturbational single-cell omicsβ150Updated 2 years ago
- β98Updated last year
- Reproducing the experiments of the paper "Deep generative modeling for single-cell transcriptomics"β56Updated 6 years ago
- β24Updated 5 months ago
- Diffusion model for gene regulatory network inference.β16Updated 9 months ago
- spatial transcriptome, single cellβ67Updated last year
- Additional code and analysis from the single-cell integration benchmarking projectβ58Updated 2 years ago
- β73Updated last year
- β49Updated 5 years ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.β49Updated 3 years ago
- Codebase for PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs) for generative modeling of single-cell time-series.β46Updated last month
- User-friendly bulk RNAseq deconvolution using simulated annealingβ17Updated last year
- Quantifying experimental perturbations at single cell resolutionβ106Updated 5 months ago
- The software of Pamona, a partial manifold alignment algorithm.β19Updated 3 years ago
- Machine learning-based approach for the inference of gene regulatory networks from expression data.β78Updated 3 years ago
- This repository implements Graph Variational Causal Inference (graphVCI), a framework that integrates prior knowledge of relational inforβ¦β15Updated 2 weeks ago
- β76Updated 7 months ago
- β23Updated 2 months ago
- β45Updated 2 years ago
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.β64Updated last month
- A collection of scripts and tools for loading, processing, and handling single cell data.β72Updated last year
- β30Updated 4 months ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leveβ¦β96Updated 6 months ago
- A probabilistic factor model for spatial transcriptomics data with subcellular resolutionβ23Updated last year
- scPerturb: A resource and a python/R tool for single-cell perturbation dataβ114Updated 2 weeks ago
- β17Updated last month
- CellBox: Interpretable Machine Learning for Perturbation Biologyβ53Updated last year