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CHOIR improves significance-based detection of cell types and states from single-cell data

Sant, Cathrine, Mucke, Lennart, Corces, M. Ryan·2025·Nature Genetics
DOI 10.1038/s41588-025-02148-8PMC PMC12108546

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  1. Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008). · DOI · Paper page
  2. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019). · DOI · Paper page
  3. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019). · DOI
  4. Dong, R. & Yuan, G. C. GiniClust3: a fast and memory-efficient tool for rare cell type identification. BMC Bioinformatics 21, 158 (2020). · DOI
  5. Peyvandipour, A., Shafi, A., Saberian, N. & Draghici, S. Identification of cell types from single cell data using stable clustering. Sci. Rep. 10, 12349 (2020). · DOI
  6. Tang, M. et al. Evaluating single-cell cluster stability using the Jaccard similarity index. Bioinformatics 37, 2212–2214 (2021). · DOI
  7. Grabski, I. N., Street, K. & Irizarry, R. A. Significance analysis for clustering with single-cell RNA-sequencing data. Nat. Methods 20, 1196–1202 (2023). · DOI
  8. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024). · DOI · Paper page
  9. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021). · DOI · Paper page
  10. Neufeld, A., Gao, L. L., Popp, J., Battle, A. & Witten, D. Inference after latent variable estimation for single-cell RNA sequencing data. Biostatistics 25, 270–287 (2024). · DOI
  11. Wang, S. K. et al. Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genom. 2, 100164 (2022). · DOI
  12. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021). · DOI · Paper page
  13. Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023). · DOI · Paper page
  14. Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017). · DOI · Paper page
  15. Lin, P., Troup, M. & Ho, J. W. CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18, 59 (2017). · DOI
  16. Wang, B., Zhu, J., Pierson, E., Ramazzotti, D. & Batzoglou, S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods 14, 414–416 (2017). · DOI
  17. Sinha, D., Kumar, A., Kumar, H., Bandyopadhyay, S. & Sengupta, D. dropClust: efficient clustering of ultra-large scRNA-seq data. Nucleic Acids Res. 46, e36 (2018). · DOI
  18. Hu, M. W. et al. PanoView: an iterative clustering method for single-cell RNA sequencing data. PLoS Comput. Biol. 15, e1007040 (2019). · DOI
  19. Yang, Y. et al. SAFE-clustering: single-cell aggregated (from Ensemble) clustering for single-cell RNA-seq data. Bioinformatics 35, 1269–1277 (2019). · DOI
  20. John, C. R., Watson, D., Barnes, M. R., Pitzalis, C. & Lewis, M. J. Spectrum: fast density-aware spectral clustering for single and multi-omic data. Bioinformatics 36, 1159–1166 (2020). · DOI
  21. Miao, Z. et al. Putative cell type discovery from single-cell gene expression data. Nat. Methods 17, 621–628 (2020). · DOI
  22. Wan, S., Kim, J. & Won, K. J. SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection. Genome Res. 30, 205–213 (2020). · DOI
  23. Tran, B., Tran, D., Nguyen, H., Ro, S. & Nguyen, T. scCAN: single-cell clustering using autoencoder and network fusion. Sci. Rep. 12, 10267 (2022). · DOI
  24. Balderson, B., Piper, M., Thor, S. & Boden, M. Cytocipher determines significantly different populations of cells in single-cell RNA-seq data. Bioinformatics 39, btad135 (2023). · DOI
  25. Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193–218 (1985). · DOI · Paper page
  26. Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020). · DOI · Paper page
  27. Yang, C., Tian, C., Hoffman, T. E., Jacobsen, N. K. & Spencer, S. L. Melanoma subpopulations that rapidly escape MAPK pathway inhibition incur DNA damage and rely on stress signalling. Nat. Commun. 12, 1747 (2021). · DOI
  28. Dave, A. et al. The Breast Cancer Single-Cell Atlas: defining cellular heterogeneity within model cell lines and primary tumors to inform disease subtype, stemness, and treatment options. Cell. Oncol. 46, 603–628 (2023). · DOI
  29. Masui, S. et al. Pluripotency governed by Sox2 via regulation of Oct3/4 expression in mouse embryonic stem cells. Nat. Cell Biol. 9, 625–635 (2007). · DOI
  30. Jin, Y. et al. Comprehensive analysis of tropomyosin isoforms in skeletal muscles by top-down proteomics. J. Muscle Res. Cell Motil. 37, 41–52 (2016). · DOI
  31. Montani, C., Gritti, L., Beretta, S., Verpelli, C. & Sala, C. The synaptic and neuronal functions of the X-linked intellectual disability protein Interleukin-1 receptor accessory protein Like 1 (IL1RAPL1). Dev. Neurobiol. 79, 85–95 (2019). · DOI
  32. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021). · DOI · Paper page
  33. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021). · DOI
  34. Vignali, D. A., Collison, L. W. & Workman, C. J. How regulatory T cells work. Nat. Rev. Immunol. 8, 523–532 (2008). · DOI · Paper page
  35. Pennock, N. D. et al. T cell responses: naive to memory and everything in between. Adv. Physiol. Educ. 37, 273–283 (2013). · DOI · Paper page
  36. Böttcher, J. P. & Reis e Sousa, C. The role of type 1 conventional dendritic cells in cancer immunity. Trends Cancer 4, 784–792 (2018). · DOI · Paper page
  37. Tao, G. et al. Collagen XIV is important for growth and structural integrity of the myocardium. J. Mol. Cell. Cardiol. 53, 626–638 (2012). · DOI
  38. Fish, J. E. & Marsden, P. A. Endothelial nitric oxide synthase: insight into cell-specific gene regulation in the vascular endothelium. Cell. Mol. Life Sci. 63, 144–162 (2006). · DOI
  39. Goncharov, N. V. et al. Markers of endothelial cells in normal and pathological conditions. Biochem. (Mosc.) Suppl. Ser. A Membr. Cell Biol. 14, 167–183 (2020).
  40. Guo, Y. & Pu, W. T. Cardiomyocyte maturation: new phase in development. Circ. Res. 126, 1086–1106 (2020). · DOI · Paper page
  41. Liodis, P. et al. Lhx6 activity is required for the normal migration and specification of cortical interneuron subtypes. J. Neurosci. 27, 3078–3089 (2007). · DOI · Paper page
  42. Rueda-Alaña, E., Martinez-Garay, I., Encinas, J. M., Molnar, Z. & Garcia-Moreno, F. Dbx1-derived pyramidal neurons are generated locally in the developing murine neocortex. Front. Neurosci. 12, 792 (2018). · DOI
  43. Fode, C. et al. A role for neural determination genes in specifying the dorsoventral identity of telencephalic neurons. Genes Dev. 14, 67–80 (2000). · DOI · Paper page
  44. Thompson, C. L. et al. A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain. Neuron 83, 309–323 (2014). · DOI
  45. Yaguchi, Y. et al. Fibroblast growth factor (FGF) gene expression in the developing cerebellum suggests multiple roles for FGF signaling during cerebellar morphogenesis and development. Dev. Dyn. 238, 2058–2072 (2009). · DOI
  46. Li, G. et al. Decision level integration of unimodal and multimodal single cell data with scTriangulate. Nat. Commun. 14, 406 (2023). · DOI
  47. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). · DOI · Paper page
  48. Peng, M. et al. Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree. Nucleic Acids Res. 49, e91 (2021). · DOI
  49. Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). · DOI · Paper page
  50. Song, D. et al. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat. Biotechnol. 42, 247–252 (2024). · DOI
  51. Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1141 (2018). · DOI · Paper page
  52. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). · DOI · Paper page
  53. Mölder, F. et al. Sustainable data analysis with Snakemake. F1000Res. 10, 33 (2021). · DOI · Paper page
  54. Stolarczyk, M., Reuter, V. P., Smith, J. P., Magee, N. E. & Sheffield, N. C. Refgenie: a reference genome resource manager. GigaScience 9, giz149 (2020). · DOI
  55. Stolarczyk, M., Xue, B. & Sheffield, N. C. Identity and compatibility of reference genomeresources. NAR Genom. Bioinform 3, lqab036 (2021). · DOI
  56. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019). · DOI
  57. Quah, F. X. & Hemberg, M. SC3s: efficient scaling of single cell consensus clustering to millions of cells. BMC Bioinformatics 23, 536 (2022). · DOI
  58. Tian, L. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. Methods 16, 479–487 (2019). · DOI
  59. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). · DOI · Paper page
  60. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018). · DOI · Paper page

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Metadata

Authors
Sant, C., Mucke, L., & Corces, M. R.
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2025-04-07
Volume
57
Issue
5
Pages
1309-1319
ISSN
  • 1061-4036
  • 1546-1718
DOI
10.1038/s41588-025-02148-8
PMC
PMC12108546
Cited by
18
References
11
Language
en
Licence
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute on Aging (P01 AG073082, U01 AG072573, R21 AG085079)
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (UM1 HG012076)

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CoChat. (2026). Citation check: CHOIR improves significance-based detection of cell types and states from single-cell data. https://app.cochat.ai/papers/10.1038/s41588-025-02148-8
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Sant, C., Mucke, L., & Corces, M. R. (2025). CHOIR improves significance-based detection of cell types and states from single-cell data. Nature Genetics. https://doi.org/10.1038/s41588-025-02148-8