995 related articles for article (PubMed ID: 30674886)
1. Single-cell RNA-seq denoising using a deep count autoencoder.
Eraslan G; Simon LM; Mircea M; Mueller NS; Theis FJ
Nat Commun; 2019 Jan; 10(1):390. PubMed ID: 30674886
[TBL] [Abstract][Full Text] [Related]
2. DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data.
Zhao S; Zhang L; Liu X
Comput Biol Med; 2022 Jul; 146():105578. PubMed ID: 35569337
[TBL] [Abstract][Full Text] [Related]
3. Data denoising with transfer learning in single-cell transcriptomics.
Wang J; Agarwal D; Huang M; Hu G; Zhou Z; Ye C; Zhang NR
Nat Methods; 2019 Sep; 16(9):875-878. PubMed ID: 31471617
[TBL] [Abstract][Full Text] [Related]
4. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
[TBL] [Abstract][Full Text] [Related]
5. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.
Chen S; Yan X; Zheng R; Li M
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36567258
[TBL] [Abstract][Full Text] [Related]
6. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.
Gan Y; Huang X; Zou G; Zhou S; Guan J
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35172334
[TBL] [Abstract][Full Text] [Related]
7. scDMAE: A Generative Denoising Model Adopted Mask Strategy for scRNA-Seq Data Recovery.
Liu W; Pan Y; Teng Z; Xu J
IEEE J Biomed Health Inform; 2024 Jun; 28(6):3772-3780. PubMed ID: 38568766
[TBL] [Abstract][Full Text] [Related]
8. Attention-based deep clustering method for scRNA-seq cell type identification.
Li S; Guo H; Zhang S; Li Y; Li M
PLoS Comput Biol; 2023 Nov; 19(11):e1011641. PubMed ID: 37948464
[TBL] [Abstract][Full Text] [Related]
9. Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data.
Su Y; Lin R; Wang J; Tan D; Zheng C
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715275
[TBL] [Abstract][Full Text] [Related]
10. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.
Wu X; Zhou Y
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901457
[TBL] [Abstract][Full Text] [Related]
11. Single-cell RNA sequencing data imputation using bi-level feature propagation.
Lee J; Yun S; Kim Y; Chen T; Kellis M; Park C
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38706317
[TBL] [Abstract][Full Text] [Related]
12. SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model.
Zheng Y; Zhong Y; Hu J; Shang X
BMC Bioinformatics; 2021 Jan; 22(1):5. PubMed ID: 33407064
[TBL] [Abstract][Full Text] [Related]
13. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data.
Talwar D; Mongia A; Sengupta D; Majumdar A
Sci Rep; 2018 Nov; 8(1):16329. PubMed ID: 30397240
[TBL] [Abstract][Full Text] [Related]
14. Single-Cell Transcriptomics of Immune Cells: Cell Isolation and cDNA Library Generation for scRNA-Seq.
Arsenio J
Methods Mol Biol; 2020; 2184():1-18. PubMed ID: 32808214
[TBL] [Abstract][Full Text] [Related]
15. Dimensionality Reduction of Single-Cell RNA Sequencing Data by Combining Entropy and Denoising AutoEncoder.
Zhu X; Li J; Lin Y; Zhao L; Wang J; Peng X
J Comput Biol; 2022 Oct; 29(10):1074-1084. PubMed ID: 35834604
[No Abstract] [Full Text] [Related]
16. Iterative point set registration for aligning scRNA-seq data.
Alavi A; Bar-Joseph Z
PLoS Comput Biol; 2020 Oct; 16(10):e1007939. PubMed ID: 33108369
[TBL] [Abstract][Full Text] [Related]
17. Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.
Xu X; Yu X; Hu G; Wang K; Zhang J; Li X
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35821114
[TBL] [Abstract][Full Text] [Related]
18. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
19. scGCL: an imputation method for scRNA-seq data based on graph contrastive learning.
Xiong Z; Luo J; Shi W; Liu Y; Xu Z; Wang B
Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36825817
[TBL] [Abstract][Full Text] [Related]
20. scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network.
Wang J; Xia J; Wang H; Su Y; Zheng CH
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36631401
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]