213 related articles for article (PubMed ID: 36982574)
1. Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders.
Wang X; Zhang C; Wang L; Zheng P
Int J Mol Sci; 2023 Mar; 24(6):. PubMed ID: 36982574
[TBL] [Abstract][Full Text] [Related]
2. scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.
Wang HY; Zhao JP; Zheng CH; Su YS
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36592058
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.
Wang T; Johnson TS; Shao W; Lu Z; Helm BR; Zhang J; Huang K
Genome Biol; 2019 Aug; 20(1):165. PubMed ID: 31405383
[TBL] [Abstract][Full Text] [Related]
5. ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.
Wang Y; Liu T; Zhao H
Bioinformatics; 2022 Aug; 38(16):3942-3949. PubMed ID: 35771600
[TBL] [Abstract][Full Text] [Related]
6. Learning deep features and topological structure of cells for clustering of scRNA-sequencing data.
Wang H; Ma X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35302164
[TBL] [Abstract][Full Text] [Related]
7. BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.
Zhan X; Yin Y; Zhang H
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38439545
[TBL] [Abstract][Full Text] [Related]
8. A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis.
Lin E; Mukherjee S; Kannan S
BMC Bioinformatics; 2020 Feb; 21(1):64. PubMed ID: 32085701
[TBL] [Abstract][Full Text] [Related]
9. scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.
Wang H; Zhao J; Zheng C; Su Y
PLoS Comput Biol; 2022 Dec; 18(12):e1010772. PubMed ID: 36534702
[TBL] [Abstract][Full Text] [Related]
10. Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders.
Wang Y; Zhao H
PLoS Comput Biol; 2022 Apr; 18(4):e1010025. PubMed ID: 35363784
[TBL] [Abstract][Full Text] [Related]
11. IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks.
Wang X; Zhang C; Zhang Y; Meng X; Zhang Z; Shi X; Song T
Int J Mol Sci; 2022 Feb; 23(4):. PubMed ID: 35216199
[TBL] [Abstract][Full Text] [Related]
12. Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Flores M; Liu Z; Zhang T; Hasib MM; Chiu YC; Ye Z; Paniagua K; Jo S; Zhang J; Gao SJ; Jin YF; Chen Y; Huang Y
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34929734
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.
Monnier L; Cournède PH
PLoS Comput Biol; 2024 Feb; 20(2):e1011880. PubMed ID: 38386700
[TBL] [Abstract][Full Text] [Related]
15. One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.
Wang CX; Zhang L; Wang B
Genome Biol; 2022 Apr; 23(1):102. PubMed ID: 35443717
[TBL] [Abstract][Full Text] [Related]
16. HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.
Wang X; Wang J; Zhang H; Huang S; Yin Y
Bioinformatics; 2022 Feb; 38(5):1295-1303. PubMed ID: 34864918
[TBL] [Abstract][Full Text] [Related]
17. Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation.
Chen L; Zhai Y; He Q; Wang W; Deng M
Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32674393
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. scSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder.
Zhao JP; Hou TS; Su Y; Zheng CH
Methods; 2022 Dec; 208():66-74. PubMed ID: 36377123
[TBL] [Abstract][Full Text] [Related]
20. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
