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SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species
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“To filter extremely low-quality or damaged cells, we kept cells with over 200 genes expressed (i.e., expression vector with non-zero value count > 200) for pre-training by using the Seurat 61 and Scanpy 62 packages.”
From Large-scale foundation model on single-cell transcriptomics (2024) · cites this paper as [62]
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Cited by
Sorted by citations
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628
Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628
Molecular logic of cellular diversification in the mouse cerebral cortex
Di Bella, D. J., Habibi, E., Stickels, R. R., Scalia, G., Brown, J., Yadollahpour, P., Yang, S. M., Abbate, C., Biancalani, T., Macosko, E. Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03670-5 · Cited by 464
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
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
Metadata
- Authors
- Tan, Y., & Cahan, P.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 9
- Issue
- 2
- Pages
- 207-213.e2
- ISSN
- 2405-4712
- PMC
- PMC6715530
- Cited by
- 368
- References
- 11
- Language
- en
- Licence
- Funders
- National Institutes of Health
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