The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Open accessAbstract
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma. CellAssign uses a probabilistic model to assign single cells to a given cell type defined by known marker genes, enabling automated annotation of cell types present in a tumor microenvironment.
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Cites
The Molecular Signatures Database Hallmark Gene Set Collection
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Robust enumeration of cell subsets from tissue expression profiles
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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UMAP: Uniform Manifold Approximation and Projection
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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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
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 5,605
A pathology atlas of the human cancer transcriptome
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The Reactome Pathway Knowledgebase
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Cited by
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,694
“While several methods have 122 been proposed for cell annotation [30, 31, 32], they often require dimension reduction prior to 123 model input, which can lead to information loss.”
MethodA Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789
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
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158
Enfield, K. S. S., Colliver, E., Lee, C., Magness, A., Moore, D. A., Sivakumar, M., Grigoriadis, K., Pich, O., Karasaki, T., Hobson, P. S., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1380 · Cited by 151
Metadata
- Authors
- Zhang, A. W., O’Flanagan, C., Chavez, E. A., Lim, J. L. P., Ceglia, N., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., Lai, D., Mottok, A., Sarkozy, C., Chong, L., Aoki, T., Wang, X., Weng, A. P., McAlpine, J. N., Aparicio, S., Steidl, C., Campbell, K. R., & Shah, S. P.
- Venue
- Nature Methods
- Published
- 2019-09-09
- Volume
- 16
- Issue
- 10
- Pages
- 1007-1015
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2973034691
- Cited by
- 375
- References
- 54
- FWCI
- 13.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, A. W., O’Flanagan, C., Chavez, E. A., Lim, J. L. P., Ceglia, N., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., Lai, D., Mottok, A., Sarkozy, C., Chong, L., Aoki, T., Wang, X., Weng, A. P., McAlpine, J. N., Aparicio, S., . . . Shah, S. P. (2019). Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nature Methods. https://doi.org/10.1038/s41592-019-0529-1
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
