These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

905 related articles for article (PubMed ID: 27083874)

  • 41. Studying hematopoiesis using single-cell technologies.
    Ye F; Huang W; Guo G
    J Hematol Oncol; 2017 Jan; 10(1):27. PubMed ID: 28109325
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Single cell genomics: advances and future perspectives.
    Macaulay IC; Voet T
    PLoS Genet; 2014 Jan; 10(1):e1004126. PubMed ID: 24497842
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Lessons from single cell sequencing in CNS cell specification and function.
    Li Z; Tyler WA; Haydar TF
    Curr Opin Genet Dev; 2020 Dec; 65():138-143. PubMed ID: 32679535
    [TBL] [Abstract][Full Text] [Related]  

  • 44. 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]  

  • 45. Cell type discovery and representation in the era of high-content single cell phenotyping.
    Bakken T; Cowell L; Aevermann BD; Novotny M; Hodge R; Miller JA; Lee A; Chang I; McCorrison J; Pulendran B; Qian Y; Schork NJ; Lasken RS; Lein ES; Scheuermann RH
    BMC Bioinformatics; 2017 Dec; 18(Suppl 17):559. PubMed ID: 29322913
    [TBL] [Abstract][Full Text] [Related]  

  • 46. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.
    Jiang L; Chen H; Pinello L; Yuan GC
    Genome Biol; 2016 Jul; 17(1):144. PubMed ID: 27368803
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.
    Zhao C; Hu S; Huo X; Zhang Y
    PLoS One; 2017; 12(7):e0180583. PubMed ID: 28671995
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos.
    Liu N; Liu L; Pan X
    Cell Mol Life Sci; 2014 Jul; 71(14):2707-15. PubMed ID: 24652479
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis.
    Saadatpour A; Guo G; Orkin SH; Yuan GC
    Genome Biol; 2014 Dec; 15(12):525. PubMed ID: 25517911
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
    Buettner F; Natarajan KN; Casale FP; Proserpio V; Scialdone A; Theis FJ; Teichmann SA; Marioni JC; Stegle O
    Nat Biotechnol; 2015 Feb; 33(2):155-60. PubMed ID: 25599176
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Concepts and limitations for learning developmental trajectories from single cell genomics.
    Tritschler S; Büttner M; Fischer DS; Lange M; Bergen V; Lickert H; Theis FJ
    Development; 2019 Jun; 146(12):. PubMed ID: 31249007
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Exploiting Single-Cell Tools in Gene and Cell Therapy.
    Bode D; Cull AH; Rubio-Lara JA; Kent DG
    Front Immunol; 2021; 12():702636. PubMed ID: 34322133
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example.
    Schmidt M; Loeffler-Wirth H; Binder H
    Genes (Basel); 2020 Oct; 11(10):. PubMed ID: 33081343
    [TBL] [Abstract][Full Text] [Related]  

  • 54. End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data.
    Derr A; Yang C; Zilionis R; Sergushichev A; Blodgett DM; Redick S; Bortell R; Luban J; Harlan DM; Kadener S; Greiner DL; Klein A; Artyomov MN; Garber M
    Genome Res; 2016 Oct; 26(10):1397-1410. PubMed ID: 27470110
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Single-Cell Transcriptional Analysis.
    Wu AR; Wang J; Streets AM; Huang Y
    Annu Rev Anal Chem (Palo Alto Calif); 2017 Jun; 10(1):439-462. PubMed ID: 28301747
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Advances and applications of single-cell sequencing technologies.
    Wang Y; Navin NE
    Mol Cell; 2015 May; 58(4):598-609. PubMed ID: 26000845
    [TBL] [Abstract][Full Text] [Related]  

  • 57. The Significance of Single-Cell Biomedicine in Stem Cells.
    Zhuge W; Yan F; Zhu Z; Wang X
    Adv Exp Med Biol; 2018; 1068():187-195. PubMed ID: 29943306
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Breaking the population barrier by single cell analysis: one host against one pathogen.
    Mills E; Avraham R
    Curr Opin Microbiol; 2017 Apr; 36():69-75. PubMed ID: 28214736
    [TBL] [Abstract][Full Text] [Related]  

  • 59. A brief review of single-cell transcriptomic technologies.
    Kalisky T; Oriel S; Bar-Lev TH; Ben-Haim N; Trink A; Wineberg Y; Kanter I; Gilad S; Pyne S
    Brief Funct Genomics; 2018 Jan; 17(1):64-76. PubMed ID: 28968725
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Self-assembling manifolds in single-cell RNA sequencing data.
    Tarashansky AJ; Xue Y; Li P; Quake SR; Wang B
    Elife; 2019 Sep; 8():. PubMed ID: 31524596
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 46.