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-------------------------------------------------------------------------------- 1 | # Single-Cell Model Collections 2 | ![](https://github.com/xianglin226/Benchmarking-Single-Cell-Perturbation/blob/master/mach.jpg) 3 | Many models for single-cell perturbation data coming out! 4 | ## Models developed for single-cell perturbation data 5 | | Name | Year | Journal | Title | 6 | | -------|:-------------:| -----:|--------:| 7 | | [Rachel et al](https://psb.stanford.edu/psb-online/proceedings/psb18/hodos.pdf) |2018 |Pacific Symposium on Biocomputing 2018|Cell-specific prediction and application of drug-induced gene expression profiles 8 | | [scGEN](https://www.nature.com/articles/s41592-019-0494-8)|2019 |Nature Method|scGen predicts single-cell perturbation responses 9 | | [DTD](https://dl.acm.org/doi/abs/10.1145/3308558.3313476) |2019 |The World Wide Web Conference, 2019|Modeling Relational Drug-Target-Disease Interactions via Tensor Factorization with Multiple Web Sources 10 | | [CPA](https://www.embopress.org/doi/full/10.15252/msb.202211517)|2021 |Molecular system biology|Predicting cellular responses to complex perturbations in high‐throughput screens 11 | | [CellBox](https://www.cell.com/cell-systems/pdf/S2405-4712(20)30464-6.pdf)|2021 |Cell systems|CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy 12 | | [CellDrift](https://academic.oup.com/bib/article/23/5/bbac324/6673850)|2022 |BIB | CellDrift: inferring perturbation responses in temporally sampled single-cell data 13 | | [MultiCPA](https://www.biorxiv.org/content/10.1101/2022.07.08.499049v1.abstract)|2022 || MultiCPA: Multimodal Compositional Perturbation Autoencoder 14 | | [PerturbNet](https://www.biorxiv.org/content/10.1101/2022.07.20.500854v2.abstract)|2022 || PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations 15 | | [scINSIGHT](https://link.springer.com/article/10.1186/s13059-022-02649-33)|2022 |Genome biology |scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data 16 | | [scpregan](https://academic.oup.com/bioinformatics/article/38/13/3377/6593485)|2022 |Bioinformatics |scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation 17 | | [Gears](https://www.nature.com/articles/s41587-023-01905-6)|2023 |Nature Biotech| Predicting transcriptional outcomes of novel multigene perturbations with GEARS 18 | | [cycleCDR](https://arxiv.org/abs/2311.10315)|2023 || Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning 19 | | [scVIDR](https://www.cell.com/patterns/pdf/S2666-3899(23)00186-1.pdf)|2023 |Patterns| Generative modeling of single-cell gene expression for dose-dependent chemical perturbations 20 | | [Unagi](https://www.researchsquare.com/article/rs-3676579/v1)|2023 ||Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases 21 | | [CINEMA-OT](https://www.nature.com/articles/s41592-023-02040-5)| 2023 |Nature Method| Causal identification of single-cell experimental perturbation effects with CINEMA-OT 22 | | [ChemCPA](https://proceedings.neurips.cc/paper_files/paper/2022/hash/aa933b5abc1be30baece1d230ec575a7-Abstract-Conference.html)|2023 |NeurIPS 2022 |Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution 23 | | [DREEP](https://link.springer.com/article/10.1186/s12916-023-03182-1)| 2023 |BMC Medicine|Predicting drug response from single-cell expression profiles of tumours 24 | | [ontoVAE](https://academic.oup.com/bioinformatics/article/39/6/btad387/7199588)|2023 |Bioinformatics| Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations 25 | | [scDiff](https://www.biorxiv.org/content/10.1101/2023.10.13.562243v1.abstract)| 2023 || A GENERAL SINGLE-CELL ANALYSIS FRAMEWORK VIA CONDITIONAL DIFFUSION GENERATIVE MODELS 26 | | [ContrastiveVI](https://www.nature.com/articles/s41592-023-01955-3)| 2023 |Nature Method|Isolating salient variations of interest in single-cell data with contrastiveVI 27 | | [sVAE](https://proceedings.mlr.press/v213/lopez23a.html)| 2023 |PMLR | Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling 28 | | [CellOT](https://www.nature.com/articles/s41592-023-01969-x)|2023 |Nature Method |Learning single-cell perturbation responses using neural optimal transport 29 | |[MOASL](https://www.sciencedirect.com/science/article/abs/pii/S0010482523013185)| 2023| Computers in Biology and Medicine | MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature 30 | | [samsVAE](https://proceedings.neurips.cc/paper_files/paper/2023/hash/0001ca33ba34ce0351e4612b744b3936-Abstract-Conference.html)| 2024 |Advances in Neural Information Processing Systems| Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder 31 | | [Biolord](https://www.nature.com/articles/s41587-023-02079-x)| 2024 |Nature Biotech| Disentanglement of single-cell data with biolord 32 | | [Pdgrapher](https://www.biorxiv.org/content/10.1101/2024.01.03.573985v2.abstract)| 2024 | | Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks 33 | | [TAT](https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c01855)| 2024 |Journal of Chemical Information and Modeling| Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning 34 | | [scVAE](https://www.biorxiv.org/content/10.1101/2024.01.05.574421v1.abstract)| 2024 | | A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data 35 | | [Cell PaintingCNN](https://www.nature.com/articles/s41467-024-45999-1)|2024 |NC | Learning representations for image-based profiling of perturbations 36 | | [scDisInFact](https://www.nature.com/articles/s41467-024-45227-w)| 2024 |NC| scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data 37 | | [CellCap](https://www.biorxiv.org/content/10.1101/2024.03.14.585078v1.abstract)| 2024 || Modeling interpretable correspondence between cell state and perturbation response with CellCap 38 | | [CODEX](https://academic.oup.com/bioinformatics/article/40/Supplement_1/i91/7700898) | 2024 | Bioinformatics | CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations 39 | | [scFM](https://www.biorxiv.org/content/10.1101/2024.10.02.616248v1) | 2024 | | PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction 40 | | [STAMP](https://www.nature.com/articles/s43588-024-00698-1) | 2024 | NCS | Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond 41 | | [PrePR-CT](https://www.biorxiv.org/content/10.1101/2024.07.24.604816v1.full.pdf) |2024| | PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs 42 | |[PRnet](https://www.nature.com/articles/s41467-024-53457-1) |2024| NC | Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery 43 | |[TranSiGen](https://www.nature.com/articles/s41467-024-49620-3)|2024|NC|Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery 44 | |[BioDiscoveryAgent](https://arxiv.org/abs/2405.17631)| 2024| | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments 45 | |[DRSPRING](https://www.sciencedirect.com/science/article/pii/S0010482524005201)| 2024| Computers in Biology and Medicine| DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile 46 | |[PertKGE](https://www.biorxiv.org/content/10.1101/2024.04.08.588632v1.abstract)|2024|| Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics 47 | |[scRank](https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(24)00260-X)| 2024| Cell Reports Medicine | scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network 48 | |[Pertpy](https://www.biorxiv.org/content/10.1101/2024.08.04.606516v1.full.pdf)| 2024 | | Pertpy: an end-to-end framework for perturbation analysis 49 | |[CellFlow](https://www.biorxiv.org/content/10.1101/2025.04.11.648220v1)| 2025 | | CellFlow enables generative single-cell phenotype modeling with flow matching 50 | |[TxPert](https://arxiv.org/abs/2505.14919)| 2025 | | TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction 51 | 52 | ## Perturbation Datasets 53 | [SC-perturb](http://projects.sanderlab.org/scperturb/) 54 | [C-MAP](https://clue.io/) 55 | [PerturbBase](http://www.perturbase.cn/) 56 | [PerturDB](http://research.gzsys.org.cn/perturbdb/#/home) 57 | [Multiome Perturb-seq](https://www.biorxiv.org/content/10.1101/2024.07.26.605307v1.full.pdf) (paper) 58 | [Multiome: CRISPRmap](https://www.nature.com/articles/s41587-024-02386-x) (paper) 59 | [Spatial: Perturb-Fish](https://www.cell.com/cell/fulltext/S0092-8674(25)00197-7) (paper) 60 | [Spatial: PerturbView](https://www.nature.com/articles/s41587-024-02391-0) (paper) 61 | [Spatial: Perturb-map](https://www.cell.com/cell/fulltext/S0092-8674(22)00195-7?) (paper) 62 | [Spatial: Perturb-DBiT](https://www.biorxiv.org/content/10.1101/2024.11.18.624106v1.abstract) (paper) 63 | [Spatial: Spatial-perturb-seq](https://www.biorxiv.org/content/biorxiv/early/2024/12/20/2024.12.19.628843.full.pdf) (paper) 64 | [Spatial: NIS-seq](https://www.nature.com/articles/s41587-024-02516-5.pdf) (paper) 65 | 66 | ## (Pretrained) (Large)(Language) Models developed for single-cell data 67 | | Name | Year | Journal | Title | 68 | | ------------- |:-------------:| -----:|--------:| 69 | |[DeepMAPS](https://www.nature.com/articles/s41467-023-36559-0)|2021 | NC | DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer 70 | |[scBERT](https://www.nature.com/articles/s42256-022-00534-z) | 2022 | NMI | scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data 71 | |[TransCluster](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1038919/full) | 2022 | Frontiers in Genetics | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer. 72 | |[scMVP](https://link.springer.com/article/10.1186/s13059-021-02595-6)| 2022 | Genome Biology | A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data 73 | |[scGPT](https://www.nature.com/articles/s41592-024-02201-0) |2023 | NM | scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI 74 | |[Geneformer](https://www.nature.com/articles/s41586-023-06139-9) | 2023 | Nature | Transfer learning enables predictions in network biology 75 | |[CellLM](https://arxiv.org/pdf/2306.04371.pdf) | 2023 | | Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning 76 | |[Tgpt](https://www.cell.com/iscience/pdf/S2589-0042(23)00613-2.pdf) | 2023 | Iscience | Generative pretraining from large-scale transcriptomes for single-cell deciphering 77 | |[Scimilarity](https://www.biorxiv.org/content/10.1101/2023.07.18.549537v3.abstract) | 2023 | | Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages 78 | |[scFoundation](https://www.biorxiv.org/content/10.1101/2023.05.29.542705v4.abstract) | 2023 | | Large Scale Foundation Model on Single-cell Transcriptomics. 79 | |[TOSICA](https://www.nature.com/articles/s41467-023-35923-4) | 2023 | NC | Transformer for one stop interpretable cell type annotation 80 | |[CIForm](https://academic.oup.com/bib/article/24/4/bbad195/7169137) | 2023 | BIB | CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data 81 | |[scTransSort](https://www.mdpi.com/2218-273X/13/4/611) | 2023 | Biomolecules | scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings 82 | |[scMoFormer](https://dl.acm.org/doi/pdf/10.1145/3583780.3615061) | 2023 | ICIKM | Single-Cell Multimodal Prediction via Transformers 83 | |[scTranslator](https://www.biorxiv.org/content/10.1101/2023.07.04.547619v2.full.pdf) | 2023 | | A pre-trained large generative model for translating single-cell transcriptome to proteome 84 | |[Cell2Sentence](https://www.biorxiv.org/content/biorxiv/early/2024/02/15/2023.09.11.557287.full.pdf) | 2023 | | Cell2Sentence: Teaching Large Language Models the Language of Biology 85 | |[genePT](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614824/pdf/nihpp-2023.10.16.562533v2.pdf) | 2023 | | GENEPT: A SIMPLE BUT HARD-TO-BEAT FOUNDATION MODEL FOR GENES AND CELLS BUILT FROM CHATGPT 86 | |[scMulan](https://link.springer.com/chapter/10.1007/978-1-0716-3989-4_57) | 2024 |ICRCMB| scMulan: a multitask generative pre-trained language model for single-cell analysis. 87 | |[Nicheformer](https://www.biorxiv.org/content/10.1101/2024.04.15.589472v1) | 2024 | | Nicheformer: a foundation model for single-cell and spatial omics. 88 | |[CellPLM](https://openreview.net/forum?id=BKXvPDekud) | 2024 | ICLR | CellPLM: Pre-training of Cell Language Model Beyond Single Cells 89 | ### These tables will be periodically updated. We will build APIs for some of these models on [TDC](https://tdcommons.ai/) for benchmarking. 90 | ### [CZI](https://cellxgene.cziscience.com/) single-cell database 91 | -------------------------------------------------------------------------------- /mach.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xianglin226/Benchmarking-Single-Cell-Perturbation/233dcf77a9601f0f280755c04a7f232b1f1c293e/mach.jpg --------------------------------------------------------------------------------