1082 related articles for article (PubMed ID: 28346451)
1. SC3: consensus clustering of single-cell RNA-seq data.
Kiselev VY; Kirschner K; Schaub MT; Andrews T; Yiu A; Chandra T; Natarajan KN; Reik W; Barahona M; Green AR; Hemberg M
Nat Methods; 2017 May; 14(5):483-486. PubMed ID: 28346451
[TBL] [Abstract][Full Text] [Related]
2. SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression.
Nakamura T; Yabuta Y; Okamoto I; Aramaki S; Yokobayashi S; Kurimoto K; Sekiguchi K; Nakagawa M; Yamamoto T; Saitou M
Nucleic Acids Res; 2015 May; 43(9):e60. PubMed ID: 25722368
[TBL] [Abstract][Full Text] [Related]
3. Simulating multiple faceted variability in single cell RNA sequencing.
Zhang X; Xu C; Yosef N
Nat Commun; 2019 Jun; 10(1):2611. PubMed ID: 31197158
[TBL] [Abstract][Full Text] [Related]
4. Computational Analysis of Single-Cell RNA-Seq Data.
Alessandrì L; Cordero F; Beccuti M; Arigoni M; Calogero RA
Methods Mol Biol; 2021; 2284():289-301. PubMed ID: 33835449
[TBL] [Abstract][Full Text] [Related]
5. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
6. A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies.
Sun Z; Chen L; Xin H; Jiang Y; Huang Q; Cillo AR; Tabib T; Kolls JK; Bruno TC; Lafyatis R; Vignali DAA; Chen K; Ding Y; Hu M; Chen W
Nat Commun; 2019 Apr; 10(1):1649. PubMed ID: 30967541
[TBL] [Abstract][Full Text] [Related]
7. An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.
Mah CK; Wenzel AT; Juarez EF; Tabor T; Reich MM; Mesirov JP
F1000Res; 2018; 7():1306. PubMed ID: 31316748
[TBL] [Abstract][Full Text] [Related]
8. Accurate feature selection improves single-cell RNA-seq cell clustering.
Su K; Yu T; Wu H
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33611426
[TBL] [Abstract][Full Text] [Related]
9. Effective detection of variation in single-cell transcriptomes using MATQ-seq.
Sheng K; Cao W; Niu Y; Deng Q; Zong C
Nat Methods; 2017 Mar; 14(3):267-270. PubMed ID: 28092691
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.
Ntranos V; Kamath GM; Zhang JM; Pachter L; Tse DN
Genome Biol; 2016 May; 17(1):112. PubMed ID: 27230763
[TBL] [Abstract][Full Text] [Related]
13. A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.
Liang X; Cao L; Chen H; Wang L; Wang Y; Fu L; Tan X; Chen E; Ding Y; Tang J
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38168839
[TBL] [Abstract][Full Text] [Related]
14. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
[TBL] [Abstract][Full Text] [Related]
15. Improving replicability in single-cell RNA-Seq cell type discovery with Dune.
Roux de Bézieux H; Street K; Fischer S; Van den Berge K; Chance R; Risso D; Gillis J; Ngai J; Purdom E; Dudoit S
BMC Bioinformatics; 2024 May; 25(1):198. PubMed ID: 38789920
[TBL] [Abstract][Full Text] [Related]
16. Normalization for Single-Cell RNA-Seq Data Analysis.
Bacher R
Methods Mol Biol; 2019; 1935():11-23. PubMed ID: 30758817
[TBL] [Abstract][Full Text] [Related]
17. mbkmeans: Fast clustering for single cell data using mini-batch k-means.
Hicks SC; Liu R; Ni Y; Purdom E; Risso D
PLoS Comput Biol; 2021 Jan; 17(1):e1008625. PubMed ID: 33497379
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
[TBL] [Abstract][Full Text] [Related]
20. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
Srinivasan S; Leshchyk A; Johnson NT; Korkin D
RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]