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Challenges in unsupervised clustering of single-cell RNA-seq data
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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
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Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
Clinical and therapeutic relevance of cancer-associated fibroblasts
Chen, Y., McAndrews, K. M., & Kalluri, R. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00546-5 · Cited by 1,230
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Tsukui, T., Sun, K.-H., Wetter, J. B., Wilson-Kanamori, J. R., Hazelwood, L. A., Henderson, N. C., Adams, T. S., Schupp, J. C., Poli, S. D., Rosas, I. O., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15647-5 · Cited by 765
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524
Marsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 431
Marsh, S. E., Kamath, T., Walker, A. J., Dissing-Olesen, L., Hammond, T. R., Young, A. M. H., Abdulraouf, A., Nadaf, N., Dufort, C., Murphy, S., et al. (2020) · doi:10.1101/2020.12.03.408542 · Cited by 430
Assessment of computational methods for the analysis of single-cell ATAC-seq data
Chen, H., Lareau, C., Andreani, T., Vinyard, M. E., Garcia, S. P., Clement, K., Andrade-Navarro, M. A., Buenrostro, J. D., & Pinello, L. (2019) · Genome Biology · doi:10.1186/s13059-019-1854-5 · Cited by 332
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321
scds: computational annotation of doublets in single-cell RNA sequencing data
Bais, A. S., & Kostka, D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz698 · Cited by 308
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Youk, J., Kim, T., Evans, K. V., Jeong, Y.-I., Hur, Y., Hong, S. P., Kim, J. H., Yi, K., Kim, S. Y., Na, K. J., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.10.004 · Cited by 266
“Using ‘SC30 package (Kiselev et al., 2019), additional clustering exercise was carried out and marker genes were highlighted.”
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Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249
Challenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247
Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019) · Genome biology · doi:10.1186/s13059-019-1898-6 · Cited by 225
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145
Clustering of single-cell multi-omics data with a multimodal deep learning method
Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022) · Nature Communications · doi:10.1038/s41467-022-35031-9 · Cited by 135
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Fu, R., Gillen, A. E., Sheridan, R. M., Tian, C., Daya, M., Hao, Y., Hesselberth, J. R., & Riemondy, K. A. (2020) · F1000Research · doi:10.12688/f1000research.22969.2 · Cited by 135
Metabolomic profiling of single enlarged lysosomes
Zhu, H., Li, Q., Liao, T., Yin, X., Chen, Q., Wang, Z., Dai, M., Yi, L., Ge, S., Miao, C., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01182-8 · Cited by 103
Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data
Zhang, Y., Ma, Y., Huang, Y., Zhang, Y., Jiang, Q., Zhou, M., & Su, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.007 · Cited by 79
Automatic cell type identification methods for single-cell RNA sequencing
Xie, B., Jiang, Q., Mora, A., & Li, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.027 · Cited by 66
Single‐cell RNA sequencing in osteoarthritis
Gu, Y., Hu, Y., Zhang, H., Wang, S., Xu, K., & Su, J. (2023) · Cell Proliferation · doi:10.1111/cpr.13517 · Cited by 59
Ho, R., Workman, M. J., Mathkar, P., Wu, K., Kim, K. J., O’Rourke, J. G., Kellogg, M., Montel, V., Banuelos, M. G., Arogundade, O. A., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2020.10.010 · Cited by 62
Employing core regulatory circuits to define cell identity
Almeida, N., Chung, M. W. H., Drudi, E. M., Engquist, E. N., Hamrud, E., Isaacson, A., Tsang, V. S. K., Watt, F. M., & Spagnoli, F. M. (2021) · The EMBO Journal · doi:10.15252/embj.2020106785 · Cited by 49
Chen, W., Zhang, S., Williams, J., Ju, B., Shaner, B., Easton, J., Wu, G., & Chen, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.026 · Cited by 44
CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
Bernstein, M. N., Ma, Z., Gleicher, M., & Dewey, C. N. (2021) · iScience · doi:10.1016/j.isci.2020.101913 · Cited by 45
Network control principles for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz089 · Cited by 44
A novel strategy to dissect multifaceted macrophage function in human diseases
Karlinsey, K., Qu, L., Matz, A. J., & Zhou, B. (2022) · Journal of Leukocyte Biology · doi:10.1002/jlb.6mr0522-685r · Cited by 23
CHOIR improves significance-based detection of cell types and states from single-cell data
Sant, C., Mucke, L., & Corces, M. R. (2025) · Nature Genetics · doi:10.1038/s41588-025-02148-8 · Cited by 18
Expression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4
Metadata
- Authors
- Kiselev, V. Y., Andrews, T. S., & Hemberg, M.
- Venue
- Nature Reviews Genetics
- Published
- 2019-01-07
- Volume
- 20
- Issue
- 5
- Pages
- 273-282
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2907783748
- Cited by
- 1,160
- References
- 44
- FWCI
- 10.35
- Type
- journal article
- Language
- en
- Open access
- closed
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