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 *

481 related articles for article (PubMed ID: 31501545)

  • 21. SCMarker: Ab initio marker selection for single cell transcriptome profiling.
    Wang F; Liang S; Kumar T; Navin N; Chen K
    PLoS Comput Biol; 2019 Oct; 15(10):e1007445. PubMed ID: 31658262
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. Critical downstream analysis steps for single-cell RNA sequencing data.
    Zhang Z; Cui F; Lin C; Zhao L; Wang C; Zou Q
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822873
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. Joint profiling of chromatin accessibility and gene expression in thousands of single cells.
    Cao J; Cusanovich DA; Ramani V; Aghamirzaie D; Pliner HA; Hill AJ; Daza RM; McFaline-Figueroa JL; Packer JS; Christiansen L; Steemers FJ; Adey AC; Trapnell C; Shendure J
    Science; 2018 Sep; 361(6409):1380-1385. PubMed ID: 30166440
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A human cell atlas of fetal chromatin accessibility.
    Domcke S; Hill AJ; Daza RM; Cao J; O'Day DR; Pliner HA; Aldinger KA; Pokholok D; Zhang F; Milbank JH; Zager MA; Glass IA; Steemers FJ; Doherty D; Trapnell C; Cusanovich DA; Shendure J
    Science; 2020 Nov; 370(6518):. PubMed ID: 33184180
    [TBL] [Abstract][Full Text] [Related]  

  • 27. SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble.
    Huh R; Yang Y; Jiang Y; Shen Y; Li Y
    Nucleic Acids Res; 2020 Jan; 48(1):86-95. PubMed ID: 31777938
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A human cell atlas of fetal gene expression.
    Cao J; O'Day DR; Pliner HA; Kingsley PD; Deng M; Daza RM; Zager MA; Aldinger KA; Blecher-Gonen R; Zhang F; Spielmann M; Palis J; Doherty D; Steemers FJ; Glass IA; Trapnell C; Shendure J
    Science; 2020 Nov; 370(6518):. PubMed ID: 33184181
    [TBL] [Abstract][Full Text] [Related]  

  • 29. SCALE: modeling allele-specific gene expression by single-cell RNA sequencing.
    Jiang Y; Zhang NR; Li M
    Genome Biol; 2017 Apr; 18(1):74. PubMed ID: 28446220
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Clustering and classification methods for single-cell RNA-sequencing data.
    Qi R; Ma A; Ma Q; Zou Q
    Brief Bioinform; 2020 Jul; 21(4):1196-1208. PubMed ID: 31271412
    [TBL] [Abstract][Full Text] [Related]  

  • 31. SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data.
    Zhang Q; Liu W; Liu C; Lin SY; Guo AY
    Brief Bioinform; 2018 Nov; 19(6):1325-1336. PubMed ID: 28981576
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments.
    Leng N; Chu LF; Barry C; Li Y; Choi J; Li X; Jiang P; Stewart RM; Thomson JA; Kendziorski C
    Nat Methods; 2015 Oct; 12(10):947-950. PubMed ID: 26301841
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.
    Huang Y; Zhang P
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33611343
    [TBL] [Abstract][Full Text] [Related]  

  • 34. CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.
    Lieberman Y; Rokach L; Shay T
    PLoS One; 2018; 13(10):e0205499. PubMed ID: 30304022
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. Cell-level metadata are indispensable for documenting single-cell sequencing datasets.
    Puntambekar S; Hesselberth JR; Riemondy KA; Fu R
    PLoS Biol; 2021 May; 19(5):e3001077. PubMed ID: 33945522
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. Revolutionizing immunology with single-cell RNA sequencing.
    Chen H; Ye F; Guo G
    Cell Mol Immunol; 2019 Mar; 16(3):242-249. PubMed ID: 30796351
    [TBL] [Abstract][Full Text] [Related]  

  • 39. An interpretable framework for clustering single-cell RNA-Seq datasets.
    Zhang JM; Fan J; Fan HC; Rosenfeld D; Tse DN
    BMC Bioinformatics; 2018 Mar; 19(1):93. PubMed ID: 29523077
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.
    Chen L; He Q; Zhai Y; Deng M
    Bioinformatics; 2021 May; 37(6):775-784. PubMed ID: 33098418
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 25.