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Data denoising with transfer learning in single-cell transcriptomics.
Nat Methods. 2019 Sep;16(9):875-878. doi: 10.1038/s41592-019-0537-1. Epub 2019 Aug 30.
2
Single-cell RNA-seq denoising using a deep count autoencoder.
Nat Commun. 2019 Jan 23;10(1):390. doi: 10.1038/s41467-018-07931-2.
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Visualization of Single Cell RNA-Seq Data Using t-SNE in R.
Methods Mol Biol. 2020;2117:159-167. doi: 10.1007/978-1-0716-0301-7_8.
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Normalization of Single-Cell RNA-Seq Data.
Methods Mol Biol. 2021;2284:303-329. doi: 10.1007/978-1-0716-1307-8_17.
9
Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.
BMC Bioinformatics. 2022 Oct 25;23(Suppl 6):445. doi: 10.1186/s12859-022-04967-6.
10
DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data.
Comput Biol Med. 2022 Jul;146:105578. doi: 10.1016/j.compbiomed.2022.105578. Epub 2022 May 6.

引用本文的文献

2
Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application.
J Med Virol. 2025 Jun;97(6):e70447. doi: 10.1002/jmv.70447.
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Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer.
Int J Mol Sci. 2025 Mar 9;26(6):2447. doi: 10.3390/ijms26062447.
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Benchmarking single-cell cross-omics imputation methods for surface protein expression.
Genome Biol. 2025 Mar 4;26(1):46. doi: 10.1186/s13059-025-03514-9.
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AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.
bioRxiv. 2025 Jan 19:2024.03.23.586387. doi: 10.1101/2024.03.23.586387.
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Considerations for building and using integrated single-cell atlases.
Nat Methods. 2025 Jan;22(1):41-57. doi: 10.1038/s41592-024-02532-y. Epub 2024 Dec 13.

本文引用的文献

1
False signals induced by single-cell imputation.
F1000Res. 2018 Nov 2;7:1740. doi: 10.12688/f1000research.16613.2. eCollection 2018.
2
Single-cell RNA-seq denoising using a deep count autoencoder.
Nat Commun. 2019 Jan 23;10(1):390. doi: 10.1038/s41467-018-07931-2.
3
Deep generative modeling for single-cell transcriptomics.
Nat Methods. 2018 Dec;15(12):1053-1058. doi: 10.1038/s41592-018-0229-2. Epub 2018 Nov 30.
4
Single Cell RNA Sequencing of Rare Immune Cell Populations.
Front Immunol. 2018 Jul 4;9:1553. doi: 10.3389/fimmu.2018.01553. eCollection 2018.
5
Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.
Cell. 2018 Aug 23;174(5):1293-1308.e36. doi: 10.1016/j.cell.2018.05.060. Epub 2018 Jun 28.
6
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.
Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. Epub 2018 Jun 28.
7
Gene expression distribution deconvolution in single-cell RNA sequencing.
Proc Natl Acad Sci U S A. 2018 Jul 10;115(28):E6437-E6446. doi: 10.1073/pnas.1721085115. Epub 2018 Jun 26.
8
SAVER: gene expression recovery for single-cell RNA sequencing.
Nat Methods. 2018 Jul;15(7):539-542. doi: 10.1038/s41592-018-0033-z. Epub 2018 Jun 25.
9
DrImpute: imputing dropout events in single cell RNA sequencing data.
BMC Bioinformatics. 2018 Jun 8;19(1):220. doi: 10.1186/s12859-018-2226-y.
10
A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.
Nature. 2018 Mar 22;555(7697):524-528. doi: 10.1038/nature25980. Epub 2018 Mar 14.

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