BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

174 related articles for article (PubMed ID: 36063454)

  • 1. CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells.
    Tsuchiya T; Hori H; Ozaki H
    Bioinformatics; 2022 Oct; 38(21):4868-4877. PubMed ID: 36063454
    [TBL] [Abstract][Full Text] [Related]  

  • 2. CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.
    Zhang T; Wu Z; Li L; Ren J; Zhang Z; Wang G
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38678387
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Interpretable factor models of single-cell RNA-seq via variational autoencoders.
    Svensson V; Gayoso A; Yosef N; Pachter L
    Bioinformatics; 2020 Jun; 36(11):3418-3421. PubMed ID: 32176273
    [TBL] [Abstract][Full Text] [Related]  

  • 4. STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing.
    Sun D; Liu Z; Li T; Wu Q; Wang C
    Nucleic Acids Res; 2022 Apr; 50(7):e42. PubMed ID: 35253896
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Two-phase differential expression analysis for single cell RNA-seq.
    Wu Z; Zhang Y; Stitzel ML; Wu H
    Bioinformatics; 2018 Oct; 34(19):3340-3348. PubMed ID: 29688282
    [TBL] [Abstract][Full Text] [Related]  

  • 6. scRNABatchQC: multi-samples quality control for single cell RNA-seq data.
    Liu Q; Sheng Q; Ping J; Ramirez MA; Lau KS; Coffey RJ; Shyr Y
    Bioinformatics; 2019 Dec; 35(24):5306-5308. PubMed ID: 31373345
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Robust decomposition of cell type mixtures in spatial transcriptomics.
    Cable DM; Murray E; Zou LS; Goeva A; Macosko EZ; Chen F; Irizarry RA
    Nat Biotechnol; 2022 Apr; 40(4):517-526. PubMed ID: 33603203
    [TBL] [Abstract][Full Text] [Related]  

  • 8. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.
    Li H; Li H; Zhou J; Gao X
    Bioinformatics; 2022 Oct; 38(21):4878-4884. PubMed ID: 36063455
    [TBL] [Abstract][Full Text] [Related]  

  • 9. flexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data.
    Szkop KJ; Moss DS; Nobeli I
    Bioinformatics; 2021 Jun; 37(10):1461-1464. PubMed ID: 33051680
    [TBL] [Abstract][Full Text] [Related]  

  • 10. ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.
    Iida K; Kondo J; Wibisana JN; Inoue M; Okada M
    Bioinformatics; 2022 Sep; 38(18):4330-4336. PubMed ID: 35924984
    [TBL] [Abstract][Full Text] [Related]  

  • 11. sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling.
    Andersson A; Lundeberg J
    Bioinformatics; 2021 Sep; 37(17):2644-2650. PubMed ID: 33704427
    [TBL] [Abstract][Full Text] [Related]  

  • 12. scRMD: imputation for single cell RNA-seq data via robust matrix decomposition.
    Chen C; Wu C; Wu L; Wang X; Deng M; Xi R
    Bioinformatics; 2020 May; 36(10):3156-3161. PubMed ID: 32119079
    [TBL] [Abstract][Full Text] [Related]  

  • 13. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.
    Andreatta M; Berenstein AJ; Carmona SJ
    Bioinformatics; 2022 Apr; 38(9):2642-2644. PubMed ID: 35258562
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Isoform-level gene expression patterns in single-cell RNA-sequencing data.
    Vu TN; Wills QF; Kalari KR; Niu N; Wang L; Pawitan Y; Rantalainen M
    Bioinformatics; 2018 Jul; 34(14):2392-2400. PubMed ID: 29490015
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization.
    Jin K; Li B; Yan H; Zhang XF
    Bioinformatics; 2022 Jun; 38(12):3222-3230. PubMed ID: 35485740
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing.
    Jeong H; Liu Z
    Bioinformatics; 2020 Jul; 36(13):4021-4029. PubMed ID: 32348450
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime.
    Cui EH; Song D; Wong WK; Li JJ
    Bioinformatics; 2022 Aug; 38(16):3927-3934. PubMed ID: 35758616
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology.
    Sturm G; Finotello F; Petitprez F; Zhang JD; Baumbach J; Fridman WH; List M; Aneichyk T
    Bioinformatics; 2019 Jul; 35(14):i436-i445. PubMed ID: 31510660
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data.
    Bastien P; Bertrand F; Meyer N; Maumy-Bertrand M
    Bioinformatics; 2015 Feb; 31(3):397-404. PubMed ID: 25286920
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MOJITOO: a fast and universal method for integration of multimodal single-cell data.
    Cheng M; Li Z; Costa IG
    Bioinformatics; 2022 Jun; 38(Suppl 1):i282-i289. PubMed ID: 35758807
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.