Single cell papers with code

Single cell papers with code can not only facilitate the reproducibility of biomedical researches, but also promote our skills of analyzing single cell data.

'Papers with code' here means that authors provide necessary codes to reproduce figures or results in their papers.

(Last update: Mar 22, 2026)

Tutorial

• [code | Python] Luecken, M.D., and Theis, F.J. (2019). Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746.

Cell atlas

• [code | Python] Yin, J., Zheng, Y., Huang, Z., Zhou, W., Yuan, Y., Cai, P., Bai, Y., Yang, S., Gao, Y., Duan, S., Wang, Y., Xu, Z., Zhang, W., Zhang, X., Wei, Y., Huang, Y., Liu, Y., Wang, W., Yang, T., … Liu, C. (2026). Chinese Immune Multi-Omics Atlas. Science, 391(6781), eadt3130. https://doi.org/10.1126/science.adt3130

• [code | R] Nieto, P., Marc Elosua-Bayes, M., Trincado, J.L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A., Mereu, E., Moutinho, C., Ruiz, S., et al. (2020). A Single-Cell Tumor Immune Atlas for Precision Oncology. BioRxiv 2020.10.26.354829.

• [code | Python] Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 2020;583. https://doi.org/10.1038/s41586-020-2496-1.

• [code | R] Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).

• [code | Python and R] Reynolds, G. et al. Poised cell circuits in human skin are activated in disease. bioRxiv 2020.11.05.369363 (2020) doi:10.1101/2020.11.05.369363.

• [code | Python] Consortium TTS, Quake SR. The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans. bioRxiv 2021; 2021.07.19.452956

Developmental biology

• [code | R] Han, L., Chaturvedi, P., Kishimoto, K., Koike, H., Nasr, T., Iwasawa, K., Giesbrecht, K., Witcher, P.C., Eicher, A., Haines, L., et al. (2020). Single cell transcriptomics identifies a signaling network coordinating endoderm and mesoderm diversification during foregut organogenesis. Nat. Commun. 11.

• [code | R and Python] Wahle, P., Brancati, G., Harmel, C., He, Z., Gut, G., del Castillo, J. S., Xavier da Silveira dos Santos, A., Yu, Q., Noser, P., Fleck, J. S., Gjeta, B., Pavlinić, D., Picelli, S., Hess, M., Schmidt, G. W., Lummen, T. T. A., Hou, Y., Galliker, P., Goldblum, D., … Camp, J. G. (2023). Multimodal spatiotemporal phenotyping of human retinal organoid development. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01747-2

Neuroscience

• [code | Matlab] Hochgerner, H., Zeisel, A., Lönnerberg, P., and Linnarsson, S. (2018). Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21.

• [code | R] Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. bioRxiv 2020.12.29.424636 (2021) doi:10.1101/2020.12.29.424636.

• [code | R] Yu, Y., Zeng, Z., Xie, D. et al. Interneuron origin and molecular diversity in the human fetal brain. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00940-3

• [code | R] Ma, S., Skarica, M., Li, Q., Xu, C., Risgaard, R. D., Tebbenkamp, A. T. N., Mato-Blanco, X., Kovner, R., Krsnik, Ž., de Martin, X., Luria, V., Martí-Pérez, X., Liang, D., Karger, A., Schmidt, D. K., Gomez-Sanchez, Z., Qi, C., Gobeske, K. T., Pochareddy, S., … Sestan, N. (2022). Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science, eabo7257. https://doi.org/10.1126/science.abo7257

• [code | R and Python] Fleck, J. S., Jansen, S. M. J., Wollny, D., Zenk, F., Seimiya, M., Jain, A., Okamoto, R., Santel, M., He, Z., Camp, J. G., & Treutlein, B. (2022). Inferring and perturbing cell fate regulomes in human brain organoids. Nature. https://doi.org/10.1038/s41586-022-05279-8

• [code | R] Herring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landin, D. B., Clément, O., Echeverría, E. G., Sutton, G. J., Alvarez-Franco, A., Hou, R., Pflueger, C., McDonald, K., Polo, J. M., Forrest, A. R. R., Nowak, A. K., Voineagu, I., … Lister, R. (2022). Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell. https://doi.org/https://doi.org/10.1016/j.cell.2022.09.039

Immunology

• [code | R] Bunis, D. G. et al. Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells. Cell Rep. 34, (2021).

• [code | R] Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med 26, 842–844 (2020).

Human disease

• [code | Python and R] Lang, N. J., Gote-Schniering, J., Porras-Gonzalez, D., Yang, L., De Sadeleer, L. J., Jentzsch, R. C., Shitov, V. A., Zhou, S., Ansari, M., Agami, A., Mayr, C. H., Kashani, B. H., Chen, Y., Heumos, L., Pestoni, J. C., Geeraerts, E., Anquetil, V., Saniere, L., Wögrath, M., … Schiller, H. B. (2023). Ex vivo tissue perturbations coupled to single cell RNA-seq reveal multi-lineage cell circuit dynamics in human lung fibrogenesis. BioRxiv, 2023.01.16.524219. https://doi.org/10.1101/2023.01.16.524219.

scATAC-seq

• [code | R, Python] Li, Y.E., Preissl, S., Hou, X. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). https://doi.org/10.1038/s41586-021-03604-1

Spatial transcriptomics

• [code | R] Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. bioRxiv 2020.12.08.411686 (2020) doi:10.1101/2020.12.08.411686.

• [code | Python and R] Kanemaru, K., Cranley, J., Muraro, D., Miranda, A. M. A., Ho, S. Y., Wilbrey-Clark, A., Patrick Pett, J., Polanski, K., Richardson, L., Litvinukova, M., Kumasaka, N., Qin, Y., Jablonska, Z., Semprich, C. I., Mach, L., Dabrowska, M., Richoz, N., Bolt, L., Mamanova, L., … Teichmann, S. A. (2023). Spatially resolved multiomics of human cardiac niches. Nature. https://doi.org/10.1038/s41586-023-06311-1

Nanopore

• [code | Perl, Shell and R] Lebrigand, K., Magnone, V., Barbry, P. & Waldmann, R. High throughput error corrected Nanopore single cell transcriptome sequencing. Nat. Commun. 11, 4025 (2020).

Experimental research

• [code | R] Habermann A, Gutierrez A, Bui L, Yahn S, Winters N, Calvi C, et al. Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis. Sci Adv 2020;6:eaba1972. https://doi.org/10.1101/753806.

• [code | R] Zamboni, M., Llorens-Bobadilla, E., Magnusson, J.P., and Frisén, J. (2020). A Widespread Neurogenic Potential of Neocortical Astrocytes Is Induced by Injury. Cell Stem Cell 1–13.

• [code | Python] La Manno, G., Gyllborg, D., Codeluppi, S., Nishimura, K., Salto, C., Zeisel, A., Borm, L.E., Stott, S.R.W., Toledo, E.M., Villaescusa, J.C., et al. (2016). Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells. Cell 167, 566-580.e19.

• [code | R] Jin, X. et al. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).

Benchmark study

• [code | R] Chen, W. et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nat. Biotechnol. (2020) doi:10.1038/s41587-020-00748-9.

Trajectory analysis

• [code | Python] Plass, M., Solana, J., Alexander Wolf, F., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F.J., Kocks, C., and Rajewsky, N. (2018). Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science. 360.

Visualization

• [code] Schwartz GW, Zhou Y, Petrovic J, Fasolino M, Xu L, Shaffer SM, et al. TooManyCells identifies and visualizes relationships of single-cell clades. Nat Methods 2020. https://doi.org/10.1038/s41592-020-0748-5.

Cell type annotation

• [code | R and Python] Yang, C., Zhang, X., & Chen, J. (2025). Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data. bioRxiv. https://doi.org/10.1101/2025.04.10.647852

Perturbation

• [code | Python] Lotfollahi, M., Wolf, F.A., and Theis, F.J. (2019). scGen predicts single-cell perturbation responses. Nat. Methods 16, 715–721. https://doi.org/10.1038/s41592-019-0494-8

• [code | Python] Srivatsan, S.R., McFaline-Figueroa, J.L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H.A., Jackson, D.L., Daza, R.M., Christiansen, L., et al. (2020). Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51.

• [code | Python] Roohani, Y., Huang, K., and Leskovec, J. (2023). Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. 42, 927–935. https://doi.org/10.1038/s41587-023-01905-6

• [code | Python] Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I.L., Srivastava, S.R., Mohammadi, S., Sun, L., Moor, A.E., et al. (2023). Predicting cellular responses to complex perturbations in high-throughput screens. Mol. Syst. Biol. 19, e11517.

• [code | Python] Bunne, C., Stark, S.G., Gut, G., del Castillo, J.S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., and Rätsch, G. (2023). Learning single-cell perturbation responses using neural optimal transport. Nat. Methods 20, 1759–1768. https://doi.org/10.1038/s41592-023-01969-x

• [code | Python] Dong, M., Wang, B., Wei, J., de O. Fonseca, A.H., Perry, C.J., Frey, A., Ott, F., Ohemeng, K.K., Abolhassani, F., Bhatt, D.L., et al. (2023). Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat. Methods 20, 1769–1779.

• [code | Python] He, Z., Zhu, S., Gao, Y., and Treutlein, B. (2025). Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nat. Methods 23, 65–77.

• [code | Python] Klein, D., et al. (2025). CellFlow enables generative single-cell phenotype modeling with flow matching. bioRxiv 2025.04.11.648220.

• [code | Python] Adduri, S., et al. (2025). State: Arc Institute's first virtual cell model. bioRxiv. https://doi.org/10.1101/2025.06.26.661135

• [code | Python] Wei, Z., et al. (2025). Benchmarking algorithms for generalizable single-cell perturbation response prediction. Nat. Methods 23, 451–464. https://doi.org/10.1038/s41592-025-02980-0

• [code | Python] Heumos, L., et al. (2025). Pertpy: an end-to-end framework for perturbation analysis. Nat. Methods 23, 350–359. https://doi.org/10.1038/s41592-025-02909-7

• [code | R, Python] Ahlmann-Eltze, C., Huber, W., and Anders, S. (2025). Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Nat. Methods 22, 1657–1661. https://doi.org/10.1038/s41592-025-02772-6