SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles
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Abstract
Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering. Clusters are often assigned to different cell types based on the enriched canonical markers. However, this process is inefficient and arbitrary. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities as soon as the single-cell expression profile is input. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of the model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.
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The parameter settings of HVG selection and PCA in these methods are far from consensus and inevitably introduce artificial bias for performance evaluation.
“Furthermore, the parameter settings of HVG selection and PCA in these methods are far to reach a consensus and inevitably introduce artificial bias for performance evaluation 15- 19 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [18]
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Cites
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 · see citing works
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 · see citing works
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 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works
Olive, K. P., Jacobetz, M. A., Davidson, C. J., Gopinathan, A., McIntyre, D., Honess, D., Madhu, B., Goldgraben, M. A., Caldwell, M. E., Allard, D., et al. (2009) · Science · doi:10.1126/science.1171362 · Cited by 3,170 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500 · see citing works
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
Soares, J. V. B., Leandro, J. J. G., Junior, R. M. C., Jelinek, H. F., & Cree, M. J. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879967 · Cited by 1,495 · see citing works
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works
FUNDAMENTALS OF PLANARIAN REGENERATION
Reddien, P. W., & Alvarado, A. S. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.010403.095114 · Cited by 1,108 · see citing works
A general and flexible method for signal extraction from single-cell RNA-seq data
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718 · see citing works
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., & Batzoglou, S. (2017) · Nature Methods · doi:10.1038/nmeth.4207 · Cited by 706 · see citing works
Semi-Supervised Graph-Based Hyperspectral Image Classification
Camps‐Valls, G., Bandos, T. V., & Zhou, D. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.895416 · Cited by 622 · see citing works
Supervised Nonlinear Dimensionality Reduction for Visualization and Classification
Geng, X., Zhan, D.-C., & Zhou, Z. (2005) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.850151 · Cited by 432 · see citing works
A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development
Yao, Z., Mich, J. K., Ku, S., Menon, V., Krostag, A.-R., Martinez, R. A., Furchtgott, L., Mulholland, H., Bort, S. J., Fuqua, M. A., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.09.011 · Cited by 127 · see citing works
CellClassifier: supervised learning of cellular phenotypes
Rämö, P., Sacher, R., Snijder, B., Begemann, B., & Pelkmans, L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp524 · Cited by 84 · see citing works
Advances in neural information processing systems 8: Proceedings of the 1995 conference
(1997) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(97)90029-8 · Cited by 80 · see citing works
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Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617 · see citing works
ACTINN: automated identification of cell types in single cell RNA sequencing
Ma, F., & Pellegrini, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz592 · Cited by 252 · see citing works
Automated methods for cell type annotation on scRNA-seq data
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 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
- Authors
- Xie, P., Gao, M., Wang, C., Zhang, J., Noel, P., Yang, C., Von Hoff, D., Han, H., Zhang, M. Q., & Lin, W.
- Affiliations
- The University of Texas at Dallas (US)
- Xiamen University (CN)
- Northwest University (CN)
- Translational Genomics Research Institute (US)
- Translational Genomics Research Institute, Molecular Medicine Division, Phoenix, AZ, USA
- Corresponding author
- Zhang, Michael Q
- Lin, Wei
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-02-25
- Volume
- 47
- Issue
- 8
- Pages
- e48-e48
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6486558
- OpenAlex
- W2951737297
- Cited by
- 74
- References
- 22
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 8 · 2023: 8 · 2022: 10 · 2021: 11 · 2020: 13 · 2019: 6
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- High-level Talent Gathering Program in Hunan Province, China (2018RS3107)
- NIH (GM084198, MH109665)
- NSFC (31671384, 81890990)
- Stand Up To Cancer-Cancer Research UK-Lustgarten Foundation Pancreatic Cancer Dream Team Research (SU2C-AACR-DT-20-16)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, cell type classification, supervised learning, single-cell transcriptomics, classifier training
- MeSH terms
- Supervised Machine Learning, Animals, Humans, Pancreatic Neoplasms, Software, Gene Expression Regulation, Neoplastic, Cluster Analysis, Sequence Analysis, RNA, Cell Lineage, RNA, Small Cytoplasmic, Gene Expression Profiling, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome, Datasets as Topic
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