BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

258 related articles for article (PubMed ID: 33927416)

  • 1. Bayesian inference of gene expression states from single-cell RNA-seq data.
    Breda J; Zavolan M; van Nimwegen E
    Nat Biotechnol; 2021 Aug; 39(8):1008-1016. PubMed ID: 33927416
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An Effective Biclustering-Based Framework for Identifying Cell Subpopulations From scRNA-seq Data.
    Fang Q; Su D; Ng W; Feng J
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2249-2260. PubMed ID: 32167906
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data.
    DePasquale EAK; Schnell DJ; Van Camp PJ; Valiente-Alandí Í; Blaxall BC; Grimes HL; Singh H; Salomonis N
    Cell Rep; 2019 Nov; 29(6):1718-1727.e8. PubMed ID: 31693907
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.
    Cuevas-Diaz Duran R; Wei H; Wu J
    BMC Genomics; 2024 May; 25(1):444. PubMed ID: 38711017
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Decontamination of ambient RNA in single-cell RNA-seq with DecontX.
    Yang S; Corbett SE; Koga Y; Wang Z; Johnson WE; Yajima M; Campbell JD
    Genome Biol; 2020 Mar; 21(1):57. PubMed ID: 32138770
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.
    Han G; Yan D; Sun Z; Fang J; Chang X; Wilson L; Liu Y
    Hum Genomics; 2024 Jun; 18(1):69. PubMed ID: 38902839
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
    Wang J; Ma A; Chang Y; Gong J; Jiang Y; Qi R; Wang C; Fu H; Ma Q; Xu D
    Nat Commun; 2021 Mar; 12(1):1882. PubMed ID: 33767197
    [TBL] [Abstract][Full Text] [Related]  

  • 11. scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.
    Wang HY; Zhao JP; Zheng CH; Su YS
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36592058
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Identifying Subpopulations of Cells in Single-Cell Transcriptomic Data: A Bayesian Mixture Modeling Approach to Zero Inflation of Counts.
    Wilson T; Vo DHT; Thorne T
    J Comput Biol; 2023 Oct; 30(10):1059-1074. PubMed ID: 37871291
    [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. Single-Cell Transcriptome Analysis of T Cells.
    Van Der Byl W; Rizzetto S; Samir J; Cai C; Eltahla AA; Luciani F
    Methods Mol Biol; 2019; 2048():155-205. PubMed ID: 31396939
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring.
    Frost HR
    Nucleic Acids Res; 2020 Sep; 48(16):e94. PubMed ID: 32633778
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. A robust nonlinear low-dimensional manifold for single cell RNA-seq data.
    Verma A; Engelhardt BE
    BMC Bioinformatics; 2020 Jul; 21(1):324. PubMed ID: 32693778
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.