BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

244 related articles for article (PubMed ID: 36515467)

  • 1. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data.
    Yan L; Sun X
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36515467
    [TBL] [Abstract][Full Text] [Related]  

  • 2. EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning.
    Tu JJ; Li HS; Yan H; Zhang XF
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36610709
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics.
    Sang-Aram C; Browaeys R; Seurinck R; Saeys Y
    Elife; 2024 May; 12():. PubMed ID: 38787371
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.
    Li B; Zhang W; Guo C; Xu H; Li L; Fang M; Hu Y; Zhang X; Yao X; Tang M; Liu K; Zhao X; Lin J; Cheng L; Chen F; Xue T; Qu K
    Nat Methods; 2022 Jun; 19(6):662-670. PubMed ID: 35577954
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics.
    Li H; Zhou J; Li Z; Chen S; Liao X; Zhang B; Zhang R; Wang Y; Sun S; Gao X
    Nat Commun; 2023 Mar; 14(1):1548. PubMed ID: 36941264
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.
    Cai M; Yue M; Chen T; Liu J; Forno E; Lu X; Billiar T; Celedón J; McKennan C; Chen W; Wang J
    Bioinformatics; 2022 May; 38(11):3004-3010. PubMed ID: 35438146
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease.
    Slabowska AO; Pyke C; Hvid H; Jessen LE; Baumgart S; Das V
    Front Bioinform; 2024; 4():1352594. PubMed ID: 38601476
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.
    Chen J; Liu W; Luo T; Yu Z; Jiang M; Wen J; Gupta GP; Giusti P; Zhu H; Yang Y; Li Y
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35753702
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data.
    Tao Q; Xu Y; He Y; Luo T; Li X; Han L
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38796691
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. SpatialDWLS: accurate deconvolution of spatial transcriptomic data.
    Dong R; Yuan GC
    Genome Biol; 2021 May; 22(1):145. PubMed ID: 33971932
    [TBL] [Abstract][Full Text] [Related]  

  • 13. SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics.
    Swain AK; Pandit V; Sharma J; Yadav P
    Commun Biol; 2024 May; 7(1):639. PubMed ID: 38796505
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology.
    Zhu S; Kubota N; Wang S; Wang T; Xiao G; Hoshida Y
    bioRxiv; 2023 Dec; ():. PubMed ID: 38187541
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.
    Song Q; Su J
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33480403
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts.
    Navarro JF; Sjöstrand J; Salmén F; Lundeberg J; Ståhl PL
    Bioinformatics; 2017 Aug; 33(16):2591-2593. PubMed ID: 28398467
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.
    Chen L; Wu CT; Lin CH; Dai R; Liu C; Clarke R; Yu G; Van Eyk JE; Herrington DM; Wang Y
    Bioinformatics; 2022 Feb; 38(5):1403-1410. PubMed ID: 34904628
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding.
    Yin W; Wan Y; Zhou Y
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38557675
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Benchmarking of cell type deconvolution pipelines for transcriptomics data.
    Avila Cobos F; Alquicira-Hernandez J; Powell JE; Mestdagh P; De Preter K
    Nat Commun; 2020 Nov; 11(1):5650. PubMed ID: 33159064
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.