331 related articles for article (PubMed ID: 35524568)
1. scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.
Dai C; Jiang Y; Yin C; Su R; Zeng X; Zou Q; Nakai K; Wei L
Nucleic Acids Res; 2022 May; 50(9):4877-4899. PubMed ID: 35524568
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data.
Shi Y; Wan J; Zhang X; Yin Y
Comput Biol Med; 2023 Sep; 164():107263. PubMed ID: 37531858
[TBL] [Abstract][Full Text] [Related]
4. 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]
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. Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute.
Xu K; Cheong C; Veldsman WP; Lyu A; Cheung WK; Zhang L
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37039664
[TBL] [Abstract][Full Text] [Related]
7. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares.
Gong L; Cui X; Liu Y; Lin C; Gao Z
Comput Biol Med; 2024 Mar; 171():108225. PubMed ID: 38442556
[TBL] [Abstract][Full Text] [Related]
8. A systematic evaluation of single-cell RNA-sequencing imputation methods.
Hou W; Ji Z; Ji H; Hicks SC
Genome Biol; 2020 Aug; 21(1):218. PubMed ID: 32854757
[TBL] [Abstract][Full Text] [Related]
9. Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.
Lasri A; Shahrezaei V; Sturrock M
BMC Bioinformatics; 2022 Jun; 23(1):236. PubMed ID: 35715748
[TBL] [Abstract][Full Text] [Related]
10. Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.
You Y; Tian L; Su S; Dong X; Jabbari JS; Hickey PF; Ritchie ME
Genome Biol; 2021 Dec; 22(1):339. PubMed ID: 34906205
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.
Zhu X; Meng S; Li G; Wang J; Peng X
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317025
[TBL] [Abstract][Full Text] [Related]
13. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.
Huang Z; Wang J; Lu X; Mohd Zain A; Yu G
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36733262
[TBL] [Abstract][Full Text] [Related]
14. SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.
Qiu Y; Yan C; Zhao P; Zou Q
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37122068
[TBL] [Abstract][Full Text] [Related]
15. scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks.
Wang T; Zhao H; Xu Y; Wang Y; Shang X; Peng J; Xiao B
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37903416
[TBL] [Abstract][Full Text] [Related]
16. Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder.
Jiang J; Xu J; Liu Y; Song B; Guo X; Zeng X; Zou Q
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37088976
[TBL] [Abstract][Full Text] [Related]
17. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
Petegrosso R; Li Z; Kuang R
Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
[TBL] [Abstract][Full Text] [Related]
18. scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.
Kang Y; Zhang H; Guan J
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38600665
[TBL] [Abstract][Full Text] [Related]
19. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.
Azim R; Wang S; Dipu SA
Comput Biol Med; 2022 Jul; 146():105658. PubMed ID: 35751187
[TBL] [Abstract][Full Text] [Related]
20. Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data.
Xu J; Cui L; Zhuang J; Meng Y; Bing P; He B; Tian G; Kwok Pui C; Wu T; Wang B; Yang J
Comput Biol Med; 2022 Jul; 146():105697. PubMed ID: 35697529
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
