🧬 Best AI tools for Single-cell & foundation models (2026)
Foundation models and toolkits for single-cell RNA-seq and multi-omics: cell annotation, integration, embeddings and perturbation prediction.
19 tools · 18 open source · each entry links to its website, code and paper.
Virtual-cell model using curated biological knowledge to predict perturbation responses in unseen contexts.
Arc Institute virtual-cell model predicting transcriptomic responses to perturbations across cell contexts.
CZI cross-species generative single-cell foundation model trained on 112M cells from 12 species.
100M-parameter foundation model trained on 50M human single-cell transcriptomes.
Cross-species zero-shot universal cell embedding foundation model.
Graph-based prediction of transcriptional outcomes of genetic perturbations.
Transformer pretrained on ~30M single-cell transcriptomes for network biology predictions.
Generative pretrained transformer foundation model for single-cell multi-omics.
Sequence-based deep learning model for single-cell ATAC-seq representation and TF activity.
Multimodal omics framework for single-cell multi-omics in Python.
Automated cell type annotation with logistic regression models trained on curated atlases.
In silico gene perturbation via GRN inference from single-cell multi-omics.
Cell fate mapping using Markov chains on single-cell data.
Probabilistic deep generative models for single-cell omics (scVI, totalVI, scANVI...).
Scalable single-cell chromatin accessibility (scATAC-seq) analysis in R.
RNA velocity estimation with dynamical modeling.
Fast, sensitive batch integration of single-cell data.
Scalable Python toolkit for single-cell gene expression analysis.
R toolkit for single-cell genomics, integration and multimodal analysis.