1222 related articles for article (PubMed ID: 29949988)
21. 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]
22. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
Wu W; Ma X
Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
[TBL] [Abstract][Full Text] [Related]
23. scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.
An S; Shi J; Liu R; Chen Y; Wang J; Hu S; Xia X; Dong G; Bo X; He Z; Ying X
Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38603616
[TBL] [Abstract][Full Text] [Related]
24. SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation.
Zheng R; Li M; Liang Z; Wu FX; Pan Y; Wang J
Bioinformatics; 2019 Oct; 35(19):3642-3650. PubMed ID: 30821315
[TBL] [Abstract][Full Text] [Related]
25. Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Mieth B; Hockley JRF; Görnitz N; Vidovic MM; Müller KR; Gutteridge A; Ziemek D
Sci Rep; 2019 Dec; 9(1):20353. PubMed ID: 31889137
[TBL] [Abstract][Full Text] [Related]
26. scGAC: a graph attentional architecture for clustering single-cell RNA-seq data.
Cheng Y; Ma X
Bioinformatics; 2022 Apr; 38(8):2187-2193. PubMed ID: 35176138
[TBL] [Abstract][Full Text] [Related]
27. Single-cell data clustering based on sparse optimization and low-rank matrix factorization.
Hu Y; Li B; Chen F; Qu K
G3 (Bethesda); 2021 Jun; 11(6):. PubMed ID: 33787873
[TBL] [Abstract][Full Text] [Related]
28. 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]
29. Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.
Wu W; Ma X
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):566-575. PubMed ID: 35316190
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.
Baran Y; Doğan B
Comput Biol Med; 2023 Mar; 155():106634. PubMed ID: 36774895
[TBL] [Abstract][Full Text] [Related]
32. Cell-level somatic mutation detection from single-cell RNA sequencing.
Vu TN; Nguyen HN; Calza S; Kalari KR; Wang L; Pawitan Y
Bioinformatics; 2019 Nov; 35(22):4679-4687. PubMed ID: 31028395
[TBL] [Abstract][Full Text] [Related]
33. EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
Wei X; Li Z; Ji H; Wu H
Bioinformatics; 2022 May; 38(10):2692-2699. PubMed ID: 35561178
[TBL] [Abstract][Full Text] [Related]
34. CellMeSH: probabilistic cell-type identification using indexed literature.
Mao S; Zhang Y; Seelig G; Kannan S
Bioinformatics; 2022 Feb; 38(5):1393-1402. PubMed ID: 34893819
[TBL] [Abstract][Full Text] [Related]
35. Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.
Wang YM; Sun Y; Wang B; Wu Z; He XY; Zhao Y
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 37991248
[TBL] [Abstract][Full Text] [Related]
36. scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.
Ye P; Ye W; Ye C; Li S; Ye L; Ji G; Wu X
Bioinformatics; 2020 Feb; 36(3):789-797. PubMed ID: 31392316
[TBL] [Abstract][Full Text] [Related]
37. A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.
Peng H; Zeng X; Zhou Y; Zhang D; Nussinov R; Cheng F
PLoS Comput Biol; 2019 Feb; 15(2):e1006772. PubMed ID: 30779739
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.
Wang Z; Jin S; Liu G; Zhang X; Wang N; Wu D; Hu Y; Zhang C; Jiang Q; Xu L; Wang Y
BMC Bioinformatics; 2017 May; 18(1):270. PubMed ID: 28535748
[TBL] [Abstract][Full Text] [Related]
40. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.
Chen S; Hua K; Cui H; Jiang R
BMC Bioinformatics; 2019 May; 20(Suppl 7):0. PubMed ID: 31074382
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
