carpenter-singh-lab / 2022_Haghighi_NatureMethodsLinks
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
☆48Updated 5 months ago
Alternatives and similar repositories for 2022_Haghighi_NatureMethods
Users that are interested in 2022_Haghighi_NatureMethods are comparing it to the libraries listed below
Sorting:
- ☆34Updated 2 months ago
- ☆59Updated 10 months ago
- ☆80Updated 11 months ago
- Modeling complex perturbations with CellFlow☆64Updated this week
- SIMBA: SIngle-cell eMBedding Along with features☆63Updated 8 months ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆129Updated 4 months ago
- ☆52Updated 2 weeks ago
- A simulator for single cell multi-omics and spatial omics data that provides ground truth to benchmark a wide range of methods.☆57Updated 6 months ago
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆50Updated 3 years ago
- single cell foundation model for Gene network inference and more☆90Updated last week
- ☆14Updated 3 years ago
- ☆131Updated last year
- ☆55Updated last year
- Models and datasets for perturbational single-cell omics☆153Updated 2 years ago
- repository containing analysis scripts and auxiliary files☆34Updated 5 years ago
- ☆53Updated last year
- spatial transcriptome, single cell☆68Updated 2 years ago
- Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence☆39Updated 2 years ago
- Semi-supervised adversarial neural networks for classification of single cell transcriptomics data☆75Updated 6 months ago
- CMap Notebooks for LINCS 2020 Workshop☆46Updated 3 years ago
- ☆29Updated 4 years ago
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆34Updated this week
- ☆36Updated last month
- Decima is a Python library to train sequence models on single-cell RNA-seq data.☆40Updated this week
- ☆28Updated last month
- Biological Network Integration using Convolutions☆62Updated last year
- Knowledge-primed neural networks☆38Updated 2 years ago
- Deep Learning the T Cell Receptor Binding Specificity of Neoantigen☆82Updated 3 years ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆102Updated 10 months ago
- ☆82Updated 2 years ago