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adding scprint-2 and scprint
jkobject Dec 28, 2025
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Expand Up @@ -91,7 +91,9 @@ Be free to refer to our comprehensive survey paper on Deep Learning in Single-ce

## Pretrained Model or LLM or Foundation Model
**Refer more details to** [[foundation-model-single-cell-papers]](https://github.com/OmicsML/foundation-model-single-cell-papers)
1. [2024 BioRxiv] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper](https://www.biorxiv.org/content/10.1101/2024.07.29.605556v1)]
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
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** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
1. [2024 ICLR] **BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs** [[paper]](https://openreview.net/forum?id=jJCeMiwHdH)
1. [2023 bioRxiv] **CellPLM: Pre-training of Cell Language Model Beyond Single Cells** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1)
1. [2023 bioRxiv] **DNABERT-2: EFFICIENT FOUNDATION MODEL AND BENCHMARK FOR MULTI-SPECIES GENOME** [[paper]](https://arxiv.org/pdf/2306.15006.pdf)
Expand Down Expand Up @@ -279,21 +281,21 @@ Be free to refer to our comprehensive survey paper on Deep Learning in Single-ce
1. [2023 bioRxiv] **Universal preprocessing of single-cell genomics data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.09.14.543267v1.full.pdf)
1. [2023 Genome Biology] **Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02962-5)
1. [2023 Nature Communications] **A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics** [[paper]](https://www.nature.com/articles/s41467-023-37168-7)
1. [2023 bioRxiv] **Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.02.16.528866v1.abstract)
1. [2023 bioRxiv] **Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.01.14.524081v1)
1. [2022 Nature Communications] **Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data** [[paper]](https://www.nature.com/articles/s41467-022-30755-0)
1. [2022 Nature Methods] **Benchmarking atlas-level data integration in single-cell genomics** [[paper]](https://www.nature.com/articles/s41592-021-01336-8.pdf)
1. [2022 Nature Methods] **Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution** [[paper]](https://www.nature.com/articles/s41592-022-01480-9)
1. [2022 BioRxiv] **Benchmarking Automated Cell Type Annotation Tools for Single-cell ATAC-seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2022.10.05.511014v1)
1. [2022 Briefings in Bioinformatics] **Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data** [[paper]](https://academic.oup.com/bib/article/23/5/bbac286/6649780)
1. [2022 Nucleic Acids Research] **scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods** [[paper]](https://academic.oup.com/nar/article/50/9/4877/6582166)
1. [2021 Frontiers in Genetics] **Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms** [[paper]](https://www.frontiersin.org/articles/10.3389/fgene.2021.617282/full)
1. [2021 Nature Communications] **A benchmark study of simulation methods for single-cell RNA sequencing data** [[paper]](https://www.nature.com/articles/s41467-021-27130-w)
1. [2021 Genome Biology] **Benchmarking UMI-based single-cell RNA-seq preprocessing workflows** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02552-3)
1. [2020 Nature Methods] **Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data** [[paper]](https://www.nature.com/articles/s41592-019-0690-6)
1. [2020 Genome Biology] **A benchmark of batch-effect correction methods for single-cell RNA sequencing data** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9)
1. [2020 Nature Biotechnology] **A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples** [[paper]](https://www.nature.com/articles/s41587-020-00748-9)
1. [2019 Nature Methods] **Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments** [[paper]](https://www.nature.com/articles/s41592-019-0425-8)
2. [2023 bioRxiv] **Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.02.16.528866v1.abstract)
3. [2023 bioRxiv] **Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2023.01.14.524081v1)
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4. [2022 Nature Communications] **Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data** [[paper]](https://www.nature.com/articles/s41467-022-30755-0)
5. [2022 Nature Methods] **Benchmarking atlas-level data integration in single-cell genomics** [[paper]](https://www.nature.com/articles/s41592-021-01336-8.pdf)
6. [2022 Nature Methods] **Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution** [[paper]](https://www.nature.com/articles/s41592-022-01480-9)
7. [2022 BioRxiv] **Benchmarking Automated Cell Type Annotation Tools for Single-cell ATAC-seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2022.10.05.511014v1)
8. [2022 Briefings in Bioinformatics] **Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data** [[paper]](https://academic.oup.com/bib/article/23/5/bbac286/6649780)
9. [2022 Nucleic Acids Research] **scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods** [[paper]](https://academic.oup.com/nar/article/50/9/4877/6582166)
10. [2021 Frontiers in Genetics] **Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms** [[paper]](https://www.frontiersin.org/articles/10.3389/fgene.2021.617282/full)
11. [2021 Nature Communications] **A benchmark study of simulation methods for single-cell RNA sequencing data** [[paper]](https://www.nature.com/articles/s41467-021-27130-w)
12. [2021 Genome Biology] **Benchmarking UMI-based single-cell RNA-seq preprocessing workflows** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02552-3)
13. [2020 Nature Methods] **Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data** [[paper]](https://www.nature.com/articles/s41592-019-0690-6)
14. [2020 Genome Biology] **A benchmark of batch-effect correction methods for single-cell RNA sequencing data** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9)
15. [2020 Nature Biotechnology] **A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples** [[paper]](https://www.nature.com/articles/s41587-020-00748-9)
16. [2019 Nature Methods] **Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments** [[paper]](https://www.nature.com/articles/s41592-019-0425-8)

## Metric Design
1. [2019 Narure Methods] **A test metric for assessing single-cell RNA-seq batch correction** [[paper]](https://www.nature.com/articles/s41592-018-0254-1)
Expand All @@ -319,6 +321,9 @@ Be free to refer to our comprehensive survey paper on Deep Learning in Single-ce


## Representation Learning
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
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1. [2025 arxiv] **SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics** [[paper]](https://arxiv.org/abs/2412.01124)
1. [2023 Nature Machine Intelligence] **Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers** [[paper]](https://www.nature.com/articles/s42256-023-00757-8)
1. [2023 Genome Biology] **Correcting gradient-based interpretations of deep neural networks for genomics** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02956-3)
Expand All @@ -331,6 +336,9 @@ Be free to refer to our comprehensive survey paper on Deep Learning in Single-ce


## Batch Effect Correction
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
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1. [2023 Bioinformatics] **CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity** [[paper]](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad099/7055295)
1. [2020 Genomy Biology] **A benchmark of batch-effect correction methods for single-cell RNA sequencing data** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9)
1. [2020 Nature Biotechnology] **A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples** [[paper]](https://www.nature.com/articles/s41587-020-00748-9)
Expand Down Expand Up @@ -366,6 +374,9 @@ learning on spatial transcriptomics** [[paper]](https://www.biorxiv.org/content/


## Gene Regulatory Network
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
Comment thread
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** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
1. [2023 arxiv] **DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets** [[paper]](https://arxiv.org/pdf/2302.04178.pdf)
1. [2023 Bioinformatics] **STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data** [[paper]](https://academic.oup.com/bioinformatics/article/39/4/btad165/7099621)
1. [2022 Nature Machine Intelligence] **Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks** [[paper]](https://www.nature.com/articles/s42256-022-00469-5)
Expand All @@ -379,6 +390,9 @@ learning on spatial transcriptomics** [[paper]](https://www.biorxiv.org/content/


## Imputation
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
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1. [2018 Nature Communications] **An accurate and robust imputation method scImpute for single-cell RNA-seq data** [[paper]](https://www.nature.com/articles/s41467-018-03405-7)
1. [2019 Genome Biology] **DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data** [[paper]](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1837-6)
1. [2018 Cell] **Recovering Gene Interactions from Single-Cell Data Using Data Diffusion** [[paper]](https://www.cell.com/cell/fulltext/S0092-8674(18)30724-4)
Expand Down Expand Up @@ -410,6 +424,9 @@ and visualization of spatial expression data** [[Tool]](https://genomebiology.bi


## Reference Embedding or Transfer Learning
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
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1. [2019 Nature Methods] **Data denoising with transfer learning in single-cell transcriptomics** [[paper]](https://www.nature.com/articles/s41592-019-0537-1)
1. [2018 Nature Methods] **Deep generative modeling for single-cell transcriptomics** [[paper]](https://www.nature.com/articles/s41592-018-0229-2)
1. [2020 Bioinformatics] **Conditional out-of-distribution generation for unpaired data using transfer VAE** [[paper]](https://academic.oup.com/bioinformatics/article/36/Supplement_2/i610/6055927?guestAccessKey=71253caa-1779-40e8-8597-c217db539fb5&login=false)
Expand Down Expand Up @@ -453,6 +470,9 @@ contrastive fine-tuning** [[paper]](https://www.biorxiv.org/content/10.1101/2021
1. [2019 Science] **Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution** [[paper]](https://doi.org/10.1126/science.aaw1219)

## Cell Type Annotation
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
Comment thread
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** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
1. [2025 BioRxiv] **Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data** [[paper]](https://www.biorxiv.org/content/10.1101/2025.04.10.647852v1) [[code]](https://github.com/cafferychen777/mLLMCelltype)
1. [2023 biorxiv] **Scaling cross-tissue single-cell annotation models** [[paper]](https://www.biorxiv.org/content/10.1101/2023.10.07.561331v1.full.pdf)
1. [2023 Nature Methods] **Multi-layered maps of neuropil with segmentation-guided contrastive learning** [[paper]](https://www.nature.com/articles/s41592-023-02059-8)
Expand Down Expand Up @@ -500,6 +520,9 @@ NOTE: annotated reference cell graph + query cell graph
--->

## Disease Prediction
2. [2025 Biorxiv] **scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
** [[paper]](https://www.biorxiv.org/content/10.64898/2025.12.11.693702v2)
2. [2025 Nature Communications] **scPRINT: pre-training on 50 million cells allows robust gene network predictions** [[paper]](https://www.nature.com/articles/s41467-025-58699-1)
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1. [2024 Nature Biotechnology] **Can single-cell biology realize the promise of precision medicine?** [[paper]](https://www.nature.com/articles/s41587-024-02138-x)
1. [2018 IJCAI] **Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification** [[paper]](https://www.ijcai.org/Proceedings/2018/490)
1. [2021 NPJ Digital Medicine] **DeePaN - A deep patient graph convolutional network integratingclinico-genomic evidence to stratify lung cancers benefiting from immunotherapy** [[paper]](https://www.nature.com/articles/s41746-021-00381-z)
Expand Down