337 related articles for article (PubMed ID: 33741686)
1. Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching.
Chen M; Zhan Q; Mu Z; Wang L; Zheng Z; Miao J; Zhu P; Li YI
Genome Res; 2021 Apr; 31(4):698-712. PubMed ID: 33741686
[TBL] [Abstract][Full Text] [Related]
2. A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.
Das S; Rai A; Merchant ML; Cave MC; Rai SN
Genes (Basel); 2021 Dec; 12(12):. PubMed ID: 34946896
[TBL] [Abstract][Full Text] [Related]
3. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
4. Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations.
Shin D; Lee W; Lee JH; Bang D
Sci Adv; 2019 May; 5(5):eaav2249. PubMed ID: 31106268
[TBL] [Abstract][Full Text] [Related]
5. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.
Holland CH; Tanevski J; Perales-Patón J; Gleixner J; Kumar MP; Mereu E; Joughin BA; Stegle O; Lauffenburger DA; Heyn H; Szalai B; Saez-Rodriguez J
Genome Biol; 2020 Feb; 21(1):36. PubMed ID: 32051003
[TBL] [Abstract][Full Text] [Related]
6. Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Wang W; Tan H; Sun M; Han Y; Chen W; Qiu S; Zheng K; Wei G; Ni T
Nucleic Acids Res; 2021 May; 49(9):e54. PubMed ID: 33619563
[TBL] [Abstract][Full Text] [Related]
7. Identifying cell types to interpret scRNA-seq data: how, why and more possibilities.
Wang Z; Ding H; Zou Q
Brief Funct Genomics; 2020 Jul; 19(4):286-291. PubMed ID: 32232401
[TBL] [Abstract][Full Text] [Related]
8. MLG: multilayer graph clustering for multi-condition scRNA-seq data.
Lu S; Conn DJ; Chen S; Johnson KD; Bresnick EH; Keleş S
Nucleic Acids Res; 2021 Dec; 49(22):e127. PubMed ID: 34581807
[TBL] [Abstract][Full Text] [Related]
9. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
[TBL] [Abstract][Full Text] [Related]
10. Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis.
Zhu M; Lai Y
J Comput Biol; 2022 Jul; 29(7):634-649. PubMed ID: 35575729
[TBL] [Abstract][Full Text] [Related]
11. Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.
Jiang H; Huang Y; Li Q
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35419595
[TBL] [Abstract][Full Text] [Related]
12. An active learning approach for clustering single-cell RNA-seq data.
Lin X; Liu H; Wei Z; Roy SB; Gao N
Lab Invest; 2022 Mar; 102(3):227-235. PubMed ID: 34244616
[TBL] [Abstract][Full Text] [Related]
13. FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets.
Ming J; Lin Z; Zhao J; Wan X; ; Yang C; Wu AR
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35561293
[TBL] [Abstract][Full Text] [Related]
14. Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB; Kostka D
Bioinformatics; 2018 Jul; 34(13):i79-i88. PubMed ID: 29950006
[TBL] [Abstract][Full Text] [Related]
15. A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis.
Lin E; Mukherjee S; Kannan S
BMC Bioinformatics; 2020 Feb; 21(1):64. PubMed ID: 32085701
[TBL] [Abstract][Full Text] [Related]
16. A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data.
Qi R; Wu J; Guo F; Xu L; Zou Q
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33003206
[TBL] [Abstract][Full Text] [Related]
17. Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis.
Zhang Y; Kim MS; Reichenberger ER; Stear B; Taylor DM
PLoS Comput Biol; 2020 Apr; 16(4):e1007794. PubMed ID: 32339163
[TBL] [Abstract][Full Text] [Related]
18. Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing.
Gupta RK; Kuznicki J
Cells; 2020 Jul; 9(8):. PubMed ID: 32707839
[TBL] [Abstract][Full Text] [Related]
19. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
Elyanow R; Dumitrascu B; Engelhardt BE; Raphael BJ
Genome Res; 2020 Feb; 30(2):195-204. PubMed ID: 31992614
[TBL] [Abstract][Full Text] [Related]
20. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]