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 *

151 related articles for article (PubMed ID: 33630737)

  • 1. TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering.
    Gan Y; Li N; Guo C; Zou G; Guan J; Zhou S
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2512-2522. PubMed ID: 33630737
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
    Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
    Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.
    Gan Y; Guo C; Guo W; Xu G; Zou G
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35696651
    [TBL] [Abstract][Full Text] [Related]  

  • 4. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
    Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
    Srinivasan S; Leshchyk A; Johnson NT; Korkin D
    RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
    [TBL] [Abstract][Full Text] [Related]  

  • 6. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.
    Malec M; Kurban H; Dalkilic M
    BMC Bioinformatics; 2022 Jul; 23(1):291. PubMed ID: 35869420
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Review of single-cell RNA-seq data clustering for cell-type identification and characterization.
    Zhang S; Li X; Lin J; Lin Q; Wong KC
    RNA; 2023 May; 29(5):517-530. PubMed ID: 36737104
    [TBL] [Abstract][Full Text] [Related]  

  • 8. One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.
    Wang CX; Zhang L; Wang B
    Genome Biol; 2022 Apr; 23(1):102. PubMed ID: 35443717
    [TBL] [Abstract][Full Text] [Related]  

  • 9. TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.
    Xie J; Yin Y; Wang J
    Interdiscip Sci; 2021 Dec; 13(4):652-665. PubMed ID: 34109565
    [TBL] [Abstract][Full Text] [Related]  

  • 10. jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.
    Wu W; Liu Z; Ma X
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33535230
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.
    Zhang Y; Tran D; Nguyen T; Dascalu SM; Harris FC
    BMC Bioinformatics; 2023 Feb; 24(1):55. PubMed ID: 36803767
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Single cell RNA-seq data clustering using TF-IDF based methods.
    Moussa M; Măndoiu II
    BMC Genomics; 2018 Aug; 19(Suppl 6):569. PubMed ID: 30367575
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.
    Wei N; Nie Y; Liu L; Zheng X; Wu HJ
    PLoS Comput Biol; 2022 Dec; 18(12):e1010753. PubMed ID: 36469543
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
    Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
    Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
    [TBL] [Abstract][Full Text] [Related]  

  • 15. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.
    Jin S; MacLean AL; Peng T; Nie Q
    Bioinformatics; 2018 Jun; 34(12):2077-2086. PubMed ID: 29415263
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.
    Tran TN; Bader GD
    PLoS Comput Biol; 2020 Sep; 16(9):e1008205. PubMed ID: 32903255
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SC3: consensus clustering of single-cell RNA-seq data.
    Kiselev VY; Kirschner K; Schaub MT; Andrews T; Yiu A; Chandra T; Natarajan KN; Reik W; Barahona M; Green AR; Hemberg M
    Nat Methods; 2017 May; 14(5):483-486. PubMed ID: 28346451
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets.
    Song Q; Su J; Miller LD; Zhang W
    Genomics Proteomics Bioinformatics; 2021 Apr; 19(2):330-341. PubMed ID: 33359676
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Single-cell RNA-seq clustering: datasets, models, and algorithms.
    Peng L; Tian X; Tian G; Xu J; Huang X; Weng Y; Yang J; Zhou L
    RNA Biol; 2020 Jun; 17(6):765-783. PubMed ID: 32116127
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.