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 *

1303 related articles for article (PubMed ID: 24836921)

  • 1. Bayesian approach to single-cell differential expression analysis.
    Kharchenko PV; Silberstein L; Scadden DT
    Nat Methods; 2014 Jul; 11(7):740-2. PubMed ID: 24836921
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Beyond comparisons of means: understanding changes in gene expression at the single-cell level.
    Vallejos CA; Richardson S; Marioni JC
    Genome Biol; 2016 Apr; 17():70. PubMed ID: 27083558
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data.
    Jia C; Hu Y; Kelly D; Kim J; Li M; Zhang NR
    Nucleic Acids Res; 2017 Nov; 45(19):10978-10988. PubMed ID: 29036714
    [TBL] [Abstract][Full Text] [Related]  

  • 4. BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.
    Vallejos CA; Marioni JC; Richardson S
    PLoS Comput Biol; 2015 Jun; 11(6):e1004333. PubMed ID: 26107944
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Quality Control of Single-Cell RNA-seq.
    Jiang P
    Methods Mol Biol; 2019; 1935():1-9. PubMed ID: 30758816
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments.
    Shirai M; Arikawa K; Taniguchi K; Tanabe M; Sakai T
    Sci Rep; 2016 Nov; 6():36014. PubMed ID: 27876759
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An empirical Bayes method for differential expression analysis of single cells with deep generative models.
    Boyeau P; Regier J; Gayoso A; Jordan MI; Lopez R; Yosef N
    Proc Natl Acad Sci U S A; 2023 May; 120(21):e2209124120. PubMed ID: 37192164
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.
    Huang Y; Sanguinetti G
    Methods Mol Biol; 2019; 1935():175-185. PubMed ID: 30758827
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.
    Wang Z; Jin S; Liu G; Zhang X; Wang N; Wu D; Hu Y; Zhang C; Jiang Q; Xu L; Wang Y
    BMC Bioinformatics; 2017 May; 18(1):270. PubMed ID: 28535748
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression.
    Kim JK; Kolodziejczyk AA; Ilicic T; Teichmann SA; Marioni JC
    Nat Commun; 2015 Oct; 6():8687. PubMed ID: 26489834
    [TBL] [Abstract][Full Text] [Related]  

  • 11. BEARscc determines robustness of single-cell clusters using simulated technical replicates.
    Severson DT; Owen RP; White MJ; Lu X; Schuster-Böckler B
    Nat Commun; 2018 Mar; 9(1):1187. PubMed ID: 29567991
    [TBL] [Abstract][Full Text] [Related]  

  • 12. BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.
    Gu J; Wang X; Halakivi-Clarke L; Clarke R; Xuan J
    BMC Bioinformatics; 2014; 15 Suppl 9(Suppl 9):S6. PubMed ID: 25252852
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data.
    Sanchez-Castillo M; Blanco D; Tienda-Luna IM; Carrion MC; Huang Y
    Bioinformatics; 2018 Mar; 34(6):964-970. PubMed ID: 29028984
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.
    Lun AT; Bach K; Marioni JC
    Genome Biol; 2016 Apr; 17():75. PubMed ID: 27122128
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. A Bayesian factorization method to recover single-cell RNA sequencing data.
    Wen ZH; Langsam JL; Zhang L; Shen W; Zhou X
    Cell Rep Methods; 2022 Jan; 2(1):100133. PubMed ID: 35474868
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations.
    van den Brink SC; Sage F; Vértesy Á; Spanjaard B; Peterson-Maduro J; Baron CS; Robin C; van Oudenaarden A
    Nat Methods; 2017 Sep; 14(10):935-936. PubMed ID: 28960196
    [No Abstract]   [Full Text] [Related]  

  • 18. Linnorm: improved statistical analysis for single cell RNA-seq expression data.
    Yip SH; Wang P; Kocher JA; Sham PC; Wang J
    Nucleic Acids Res; 2017 Dec; 45(22):e179. PubMed ID: 28981748
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Single-cell mRNA quantification and differential analysis with Census.
    Qiu X; Hill A; Packer J; Lin D; Ma YA; Trapnell C
    Nat Methods; 2017 Mar; 14(3):309-315. PubMed ID: 28114287
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.
    van Dijk D; Sharma R; Nainys J; Yim K; Kathail P; Carr AJ; Burdziak C; Moon KR; Chaffer CL; Pattabiraman D; Bierie B; Mazutis L; Wolf G; Krishnaswamy S; Pe'er D
    Cell; 2018 Jul; 174(3):716-729.e27. PubMed ID: 29961576
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 66.