BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

254 related articles for article (PubMed ID: 31471617)

  • 1. Data denoising with transfer learning in single-cell transcriptomics.
    Wang J; Agarwal D; Huang M; Hu G; Zhou Z; Ye C; Zhang NR
    Nat Methods; 2019 Sep; 16(9):875-878. PubMed ID: 31471617
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Single-cell RNA-seq denoising using a deep count autoencoder.
    Eraslan G; Simon LM; Mircea M; Mueller NS; Theis FJ
    Nat Commun; 2019 Jan; 10(1):390. PubMed ID: 30674886
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.
    Wang T; Johnson TS; Shao W; Lu Z; Helm BR; Zhang J; Huang K
    Genome Biol; 2019 Aug; 20(1):165. PubMed ID: 31405383
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Visualization of Single Cell RNA-Seq Data Using t-SNE in R.
    Zhou B; Jin W
    Methods Mol Biol; 2020; 2117():159-167. PubMed ID: 31960377
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Normalization of Single-Cell RNA-Seq Data.
    Risso D
    Methods Mol Biol; 2021; 2284():303-329. PubMed ID: 33835450
    [TBL] [Abstract][Full Text] [Related]  

  • 7. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
    Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
    Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
    [TBL] [Abstract][Full Text] [Related]  

  • 8. No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells.
    McGinnis CS; Siegel DA; Xie G; Hartoularos G; Stone M; Ye CJ; Gartner ZJ; Roan NR; Lee SA
    BMC Biol; 2021 Jan; 19(1):10. PubMed ID: 33472616
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.
    Galuzzi BG; Vanoni M; Damiani C
    BMC Bioinformatics; 2022 Oct; 23(Suppl 6):445. PubMed ID: 36284276
    [TBL] [Abstract][Full Text] [Related]  

  • 10. DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data.
    Zhao S; Zhang L; Liu X
    Comput Biol Med; 2022 Jul; 146():105578. PubMed ID: 35569337
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Data Analysis in Single-Cell Transcriptome Sequencing.
    Gao S
    Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Semisupervised Generative Autoencoder for Single-Cell Data.
    Trong TN; Mehtonen J; González G; Kramer R; Hautamäki V; Heinäniemi M
    J Comput Biol; 2020 Aug; 27(8):1190-1203. PubMed ID: 31794242
    [No Abstract]   [Full Text] [Related]  

  • 14. Bias, robustness and scalability in single-cell differential expression analysis.
    Soneson C; Robinson MD
    Nat Methods; 2018 Apr; 15(4):255-261. PubMed ID: 29481549
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The Conjugation of Antibodies for the Simultaneous Detection of Surface Proteins and Transcriptome Analysis at a Single-Cell Level.
    Kleino I; Kekäläinen E; Lönnberg T
    Methods Mol Biol; 2020; 2184():31-45. PubMed ID: 32808216
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep generative modeling for single-cell transcriptomics.
    Lopez R; Regier J; Cole MB; Jordan MI; Yosef N
    Nat Methods; 2018 Dec; 15(12):1053-1058. PubMed ID: 30504886
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Biological process activity transformation of single cell gene expression for cross-species alignment.
    Ding H; Blair A; Yang Y; Stuart JM
    Nat Commun; 2019 Oct; 10(1):4899. PubMed ID: 31653878
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Single-Cell Transcriptomics of Immune Cells: Cell Isolation and cDNA Library Generation for scRNA-Seq.
    Arsenio J
    Methods Mol Biol; 2020; 2184():1-18. PubMed ID: 32808214
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries.
    Riemondy KA; Ransom M; Alderman C; Gillen AE; Fu R; Finlay-Schultz J; Kirkpatrick GD; Di Paola J; Kabos P; Sartorius CA; Hesselberth JR
    Nucleic Acids Res; 2019 Feb; 47(4):e20. PubMed ID: 30496484
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 13.