BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

152 related articles for article (PubMed ID: 33028196)

  • 1. Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data.
    Wang C; Gao X; Liu J
    BMC Bioinformatics; 2020 Oct; 21(1):440. PubMed ID: 33028196
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SAFE-clustering: Single-cell Aggregated (from Ensemble) clustering for single-cell RNA-seq data.
    Yang Y; Huh R; Culpepper HW; Lin Y; Love MI; Li Y
    Bioinformatics; 2019 Apr; 35(8):1269-1277. PubMed ID: 30202935
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.
    Liang X; Cao L; Chen H; Wang L; Wang Y; Fu L; Tan X; Chen E; Ding Y; Tang J
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38168839
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data.
    Xiao R; Lu G; Guo W; Jin S
    BMC Bioinformatics; 2020 Dec; 21(Suppl 16):540. PubMed ID: 33323107
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
    Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
    BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
    [TBL] [Abstract][Full Text] [Related]  

  • 7. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
    Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
    BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Single-cell RNA-seq data clustering: A survey with performance comparison study.
    Li R; Guan J; Zhou S
    J Bioinform Comput Biol; 2020 Aug; 18(4):2040005. PubMed ID: 32795134
    [TBL] [Abstract][Full Text] [Related]  

  • 9. SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression.
    Nakamura T; Yabuta Y; Okamoto I; Aramaki S; Yokobayashi S; Kurimoto K; Sekiguchi K; Nakagawa M; Yamamoto T; Saitou M
    Nucleic Acids Res; 2015 May; 43(9):e60. PubMed ID: 25722368
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.
    Mahalanabis A; Turinsky AL; Husić M; Christensen E; Luo P; Naidas A; Brudno M; Pugh T; Ramani AK; Shooshtari P
    Comput Struct Biotechnol J; 2022; 20():6375-6387. PubMed ID: 36420149
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Consensus-based clustering of single cells by reconstructing cell-to-cell dissimilarity.
    Wang C; Mu Z; Mou C; Zheng H; Liu J
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34553226
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.
    Osabe T; Shimizu K; Kadota K
    BMC Bioinformatics; 2021 Oct; 22(1):511. PubMed ID: 34670485
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Impact of similarity metrics on single-cell RNA-seq data clustering.
    Kim T; Chen IR; Lin Y; Wang AY; Yang JYH; Yang P
    Brief Bioinform; 2019 Nov; 20(6):2316-2326. PubMed ID: 30137247
    [TBL] [Abstract][Full Text] [Related]  

  • 14. ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering.
    Li J; Jiang W; Han H; Liu J; Liu B; Wang Y
    Comput Biol Chem; 2021 Feb; 90():107415. PubMed ID: 33307360
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A systematic performance evaluation of clustering methods for single-cell RNA-seq data.
    Duò A; Robinson MD; Soneson C
    F1000Res; 2018; 7():1141. PubMed ID: 30271584
    [TBL] [Abstract][Full Text] [Related]  

  • 16. SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.
    Wang HY; Zhao JP; Zheng CH
    Interdiscip Sci; 2021 Mar; 13(1):83-90. PubMed ID: 33475958
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions.
    Suner A
    Stat Appl Genet Mol Biol; 2019 Aug; 18(5):. PubMed ID: 31646845
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data.
    Song J; Liu Y; Zhang X; Wu Q; Gao J; Wang W; Li J; Song Y; Yang C
    Nucleic Acids Res; 2021 Feb; 49(3):e18. PubMed ID: 33305325
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of cell types from single cell data using stable clustering.
    Peyvandipour A; Shafi A; Saberian N; Draghici S
    Sci Rep; 2020 Jul; 10(1):12349. PubMed ID: 32703984
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Identifying cell types from single-cell data based on similarities and dissimilarities between cells.
    Li Y; Luo P; Lu Y; Wu FX
    BMC Bioinformatics; 2021 May; 22(Suppl 3):255. PubMed ID: 34006217
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.