290 related articles for article (PubMed ID: 29884114)
21. TsImpute: an accurate two-step imputation method for single-cell RNA-seq data.
Zheng W; Min W; Wang S
Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38039139
[TBL] [Abstract][Full Text] [Related]
22. Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data.
Zhang L; Zhang S
IEEE/ACM Trans Comput Biol Bioinform; 2020; 17(2):376-389. PubMed ID: 29994128
[TBL] [Abstract][Full Text] [Related]
23. scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation.
Li B; Jin K; Ou-Yang L; Yan H; Zhang XF
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1445-1456. PubMed ID: 35476574
[TBL] [Abstract][Full Text] [Related]
24. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization.
Jin K; Li B; Yan H; Zhang XF
Bioinformatics; 2022 Jun; 38(12):3222-3230. PubMed ID: 35485740
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. 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]
27. Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts.
Zhang L; Zhang S
J Mol Cell Biol; 2021 Apr; 13(1):29-40. PubMed ID: 33002136
[TBL] [Abstract][Full Text] [Related]
28. A posterior probability based Bayesian method for single-cell RNA-seq data imputation.
Chen S; Zheng R; Tian L; Wu FX; Li M
Methods; 2023 Aug; 216():21-38. PubMed ID: 37315825
[TBL] [Abstract][Full Text] [Related]
29. SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.
Das S; Rai SN
Genomics; 2021 May; 113(3):1308-1324. PubMed ID: 33662531
[TBL] [Abstract][Full Text] [Related]
30. Comparison of scRNA-seq data analysis method combinations.
Xu L; Xue T; Ding W; Shen L
Brief Funct Genomics; 2022 Nov; 21(6):433-440. PubMed ID: 36124658
[TBL] [Abstract][Full Text] [Related]
31. EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data.
Lu F; Lin Y; Yuan C; Zhang XF; Ou-Yang L
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2781-2787. PubMed ID: 34495837
[TBL] [Abstract][Full Text] [Related]
32. Model-based autoencoders for imputing discrete single-cell RNA-seq data.
Tian T; Min MR; Wei Z
Methods; 2021 Aug; 192():112-119. PubMed ID: 32971193
[TBL] [Abstract][Full Text] [Related]
33. Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.
Chi W; Deng M
Genes (Basel); 2020 May; 11(5):. PubMed ID: 32403260
[TBL] [Abstract][Full Text] [Related]
34. Imputation method for single-cell RNA-seq data using neural topic model.
Qi Y; Han S; Tang L; Liu L
Gigascience; 2022 Dec; 12():. PubMed ID: 38000911
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Are dropout imputation methods for scRNA-seq effective for scATAC-seq data?
Liu Y; Zhang J; Wang S; Zeng X; Zhang W
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34718405
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity.
Huang M; Ye X; Li H; Sakurai T
Front Genet; 2022; 13():952649. PubMed ID: 35910201
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. DEsingle for detecting three types of differential expression in single-cell RNA-seq data.
Miao Z; Deng K; Wang X; Zhang X
Bioinformatics; 2018 Sep; 34(18):3223-3224. PubMed ID: 29688277
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]