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 *

144 related articles for article (PubMed ID: 33613638)

  • 1. Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests.
    He Z; Pan Y; Shao F; Wang H
    Front Genet; 2021; 12():616686. PubMed ID: 33613638
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.
    Van den Berge K; Perraudeau F; Soneson C; Love MI; Risso D; Vert JP; Robinson MD; Dudoit S; Clement L
    Genome Biol; 2018 Feb; 19(1):24. PubMed ID: 29478411
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Differential expression of single-cell RNA-seq data using Tweedie models.
    Mallick H; Chatterjee S; Chowdhury S; Chatterjee S; Rahnavard A; Hicks SC
    Stat Med; 2022 Aug; 41(18):3492-3510. PubMed ID: 35656596
    [TBL] [Abstract][Full Text] [Related]  

  • 4. ZERO-INFLATED QUANTILE RANK-SCORE BASED TEST (ZIQRANK) WITH APPLICATION TO SCRNA-SEQ DIFFERENTIAL GENE EXPRESSION ANALYSIS.
    Ling W; Zhang W; Cheng B; Wei Y
    Ann Appl Stat; 2021 Dec; 15(4):1673-1696. PubMed ID: 35116085
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.
    Dadaneh SZ; de Figueiredo P; Sze SH; Zhou M; Qian X
    BMC Genomics; 2020 Sep; 21(Suppl 9):585. PubMed ID: 32900358
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.
    Choi K; Chen Y; Skelly DA; Churchill GA
    Genome Biol; 2020 Jul; 21(1):183. PubMed ID: 32718323
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Inverse weighting method with jackknife variance estimator for differential expression analysis of single-cell RNA sequencing data.
    Zhou L; Pan Q
    Comput Biol Chem; 2022 Oct; 100():107733. PubMed ID: 35926443
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Variance component testing for identifying differentially expressed genes in RNA-seq data.
    Yang S; Shao F; Duan W; Zhao Y; Chen F
    PeerJ; 2017; 5():e3797. PubMed ID: 28929020
    [TBL] [Abstract][Full Text] [Related]  

  • 9. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.
    Liu Y; Zhao J; Adams TS; Wang N; Schupp JC; Wu W; McDonough JE; Chupp GL; Kaminski N; Wang Z; Yan X
    BMC Bioinformatics; 2023 Aug; 24(1):318. PubMed ID: 37608264
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing.
    Mou T; Deng W; Gu F; Pawitan Y; Vu TN
    Front Genet; 2019; 10():1331. PubMed ID: 32010190
    [TBL] [Abstract][Full Text] [Related]  

  • 11. TWO-SIGMA: A novel two-component single cell model-based association method for single-cell RNA-seq data.
    Van Buren E; Hu M; Weng C; Jin F; Li Y; Wu D; Li Y
    Genet Epidemiol; 2021 Mar; 45(2):142-153. PubMed ID: 32989764
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data.
    Zhang W; Wei Y; Zhang D; Xu EY
    Bioinformatics; 2020 May; 36(10):3124-3130. PubMed ID: 32053182
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing.
    Chen W; Zhang S; Williams J; Ju B; Shaner B; Easton J; Wu G; Chen X
    Comput Struct Biotechnol J; 2020; 18():861-873. PubMed ID: 32322368
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects.
    Sekula M; Gaskins J; Datta S
    Biometrics; 2019 Dec; 75(4):1051-1062. PubMed ID: 31009065
    [TBL] [Abstract][Full Text] [Related]  

  • 15. lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA.
    Li Q; Yu X; Chaudhary R; Slebos RJC; Chung CH; Wang X
    BMC Genomics; 2019 Jul; 20(1):539. PubMed ID: 31266446
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An evaluation of statistical differential analysis methods in single-cell RNA-seq data.
    Li D; Zand M; Dye T; Goniewicz M; Rahman I; Xie Z
    Res Sq; 2023 Mar; ():. PubMed ID: 36993457
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Statistical methods for analysis of single-cell RNA-sequencing data.
    Das S; Rai SN
    MethodsX; 2021; 8():101580. PubMed ID: 35004214
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Detection of high variability in gene expression from single-cell RNA-seq profiling.
    Chen HI; Jin Y; Huang Y; Chen Y
    BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):508. PubMed ID: 27556924
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.
    Terra Machado D; Bernardes Brustolini OJ; Côrtes Martins Y; Grivet Mattoso Maia MA; Ribeiro de Vasconcelos AT
    PeerJ; 2023; 11():e15145. PubMed ID: 37033732
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.
    Yang Z; Ho YY
    Biometrics; 2022 Jun; 78(2):766-776. PubMed ID: 33720414
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.