BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

236 related articles for article (PubMed ID: 30715210)

  • 1. PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes.
    Papadopoulos N; Gonzalo PR; Söding J
    Bioinformatics; 2019 Sep; 35(18):3517-3519. PubMed ID: 30715210
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Reconstructing complex lineage trees from scRNA-seq data using MERLoT.
    Parra RG; Papadopoulos N; Ahumada-Arranz L; Kholtei JE; Mottelson N; Horokhovsky Y; Treutlein B; Soeding J
    Nucleic Acids Res; 2019 Sep; 47(17):8961-8974. PubMed ID: 31428793
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions.
    Gao X; Hu D; Gogol M; Li H
    Bioinformatics; 2019 Sep; 35(17):3038-3045. PubMed ID: 30649203
    [TBL] [Abstract][Full Text] [Related]  

  • 4. DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.
    Ye C; Speed TP; Salim A
    Bioinformatics; 2019 Dec; 35(24):5155-5162. PubMed ID: 31197307
    [TBL] [Abstract][Full Text] [Related]  

  • 5. scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.
    Ye P; Ye W; Ye C; Li S; Ye L; Ji G; Wu X
    Bioinformatics; 2020 Feb; 36(3):789-797. PubMed ID: 31392316
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Cell-level somatic mutation detection from single-cell RNA sequencing.
    Vu TN; Nguyen HN; Calza S; Kalari KR; Wang L; Pawitan Y
    Bioinformatics; 2019 Nov; 35(22):4679-4687. PubMed ID: 31028395
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
    Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
    Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
    [TBL] [Abstract][Full Text] [Related]  

  • 9. scDoc: correcting drop-out events in single-cell RNA-seq data.
    Ran D; Zhang S; Lytal N; An L
    Bioinformatics; 2020 Aug; 36(15):4233-4239. PubMed ID: 32365169
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.
    Aubin-Frankowski PC; Vert JP
    Bioinformatics; 2020 Sep; 36(18):4774-4780. PubMed ID: 33026066
    [TBL] [Abstract][Full Text] [Related]  

  • 11. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.
    Müller S; Cho A; Liu SJ; Lim DA; Diaz A
    Bioinformatics; 2018 Sep; 34(18):3217-3219. PubMed ID: 29897414
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.
    Su K; Wu Z; Wu H
    Bioinformatics; 2020 Dec; 36(19):4860-4868. PubMed ID: 32614380
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SPARSim single cell: a count data simulator for scRNA-seq data.
    Baruzzo G; Patuzzi I; Di Camillo B
    Bioinformatics; 2020 Mar; 36(5):1468-1475. PubMed ID: 31598633
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data.
    Xu J; Cai L; Liao B; Zhu W; Yang J
    Bioinformatics; 2020 May; 36(10):3139-3147. PubMed ID: 32073612
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions.
    Zhu K; Anastassiou D
    Bioinformatics; 2020 Jun; 36(11):3588-3589. PubMed ID: 32108864
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Quality control of single-cell RNA-seq by SinQC.
    Jiang P; Thomson JA; Stewart R
    Bioinformatics; 2016 Aug; 32(16):2514-6. PubMed ID: 27153613
    [TBL] [Abstract][Full Text] [Related]  

  • 17. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.
    Tang W; Bertaux F; Thomas P; Stefanelli C; Saint M; Marguerat S; Shahrezaei V
    Bioinformatics; 2020 Feb; 36(4):1174-1181. PubMed ID: 31584606
    [TBL] [Abstract][Full Text] [Related]  

  • 18. SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation.
    Zheng R; Li M; Liang Z; Wu FX; Pan Y; Wang J
    Bioinformatics; 2019 Oct; 35(19):3642-3650. PubMed ID: 30821315
    [TBL] [Abstract][Full Text] [Related]  

  • 19. V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.
    Lawlor N; Marquez EJ; Lee D; Ucar D
    Bioinformatics; 2020 Jun; 36(11):3582-3584. PubMed ID: 32119082
    [TBL] [Abstract][Full Text] [Related]  

  • 20. FBA: feature barcoding analysis for single cell RNA-Seq.
    Duan J; Hon GC
    Bioinformatics; 2021 Nov; 37(22):4266-4268. PubMed ID: 33999185
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.