212 related articles for article (PubMed ID: 30953530)
1. A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.
Sun S; Chen Y; Liu Y; Shang X
BMC Syst Biol; 2019 Apr; 13(Suppl 2):28. PubMed ID: 30953530
[TBL] [Abstract][Full Text] [Related]
2. SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.
Wang T; Nabavi S
Methods; 2018 Aug; 145():25-32. PubMed ID: 29702224
[TBL] [Abstract][Full Text] [Related]
3. Probabilistic count matrix factorization for single cell expression data analysis.
Durif G; Modolo L; Mold JE; Lambert-Lacroix S; Picard F
Bioinformatics; 2019 Oct; 35(20):4011-4019. PubMed ID: 30865271
[TBL] [Abstract][Full Text] [Related]
4. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data.
Wang T; Li B; Nelson CE; Nabavi S
BMC Bioinformatics; 2019 Jan; 20(1):40. PubMed ID: 30658573
[TBL] [Abstract][Full Text] [Related]
5. M3Drop: dropout-based feature selection for scRNASeq.
Andrews TS; Hemberg M
Bioinformatics; 2019 Aug; 35(16):2865-2867. PubMed ID: 30590489
[TBL] [Abstract][Full Text] [Related]
6. Identifying cell populations with scRNASeq.
Andrews TS; Hemberg M
Mol Aspects Med; 2018 Feb; 59():114-122. PubMed ID: 28712804
[TBL] [Abstract][Full Text] [Related]
7. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
[TBL] [Abstract][Full Text] [Related]
8. CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments.
Ekiz HA; Conley CJ; Stephens WZ; O'Connell RM
BMC Bioinformatics; 2020 May; 21(1):191. PubMed ID: 32414321
[TBL] [Abstract][Full Text] [Related]
9. scClustViz - Single-cell RNAseq cluster assessment and visualization.
Innes BT; Bader GD
F1000Res; 2018; 7():. PubMed ID: 31016009
[TBL] [Abstract][Full Text] [Related]
10. Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.
Hsu LL; Culhane AC
Sci Rep; 2023 Jan; 13(1):1197. PubMed ID: 36681709
[TBL] [Abstract][Full Text] [Related]
11. Differential Expression Analysis in Single-Cell Transcriptomics.
Alessandrì L; Arigoni M; Calogero R
Methods Mol Biol; 2019; 1979():425-432. PubMed ID: 31028652
[TBL] [Abstract][Full Text] [Related]
12. Robust classification of single-cell transcriptome data by nonnegative matrix factorization.
Shao C; Höfer T
Bioinformatics; 2017 Jan; 33(2):235-242. PubMed ID: 27663498
[TBL] [Abstract][Full Text] [Related]
13. Single-Cell RNA Sequencing Reveals Novel Markers of Male Pituitary Stem Cells and Hormone-Producing Cell Types.
Cheung LYM; George AS; McGee SR; Daly AZ; Brinkmeier ML; Ellsworth BS; Camper SA
Endocrinology; 2018 Dec; 159(12):3910-3924. PubMed ID: 30335147
[TBL] [Abstract][Full Text] [Related]
14. FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test.
Zhang Y; Aevermann BD; Bakken TE; Miller JA; Hodge RD; Lein ES; Scheuermann RH
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33249453
[TBL] [Abstract][Full Text] [Related]
15. NMF-mGPU: non-negative matrix factorization on multi-GPU systems.
Mejía-Roa E; Tabas-Madrid D; Setoain J; García C; Tirado F; Pascual-Montano A
BMC Bioinformatics; 2015 Feb; 16():43. PubMed ID: 25887585
[TBL] [Abstract][Full Text] [Related]
16. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization.
Zhu X; Ching T; Pan X; Weissman SM; Garmire L
PeerJ; 2017; 5():e2888. PubMed ID: 28133571
[TBL] [Abstract][Full Text] [Related]
17. INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.
Zhao K; Huang S; Lin C; Sham PC; So HC; Lin Z
PLoS Genet; 2024 Mar; 20(3):e1011189. PubMed ID: 38484017
[TBL] [Abstract][Full Text] [Related]
18. Convex nonnegative matrix factorization with manifold regularization.
Hu W; Choi KS; Wang P; Jiang Y; Wang S
Neural Netw; 2015 Mar; 63():94-103. PubMed ID: 25523040
[TBL] [Abstract][Full Text] [Related]
19. Methods and tools for spatial mapping of single-cell RNAseq clusters in Drosophila.
Mohr SE; Tattikota SG; Xu J; Zirin J; Hu Y; Perrimon N
Genetics; 2021 Apr; 217(4):. PubMed ID: 33713129
[TBL] [Abstract][Full Text] [Related]
20. CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures.
Sherman TD; Gao T; Fertig EJ
BMC Bioinformatics; 2020 Oct; 21(1):453. PubMed ID: 33054706
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]