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 *

120 related articles for article (PubMed ID: 37333291)

  • 1. RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.
    Singh R; He X; Park AK; Hardison RC; Zhu X; Li Q
    bioRxiv; 2023 Jun; ():. PubMed ID: 37333291
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.
    Miller BF; Huang F; Atta L; Sahoo A; Fan J
    Nat Commun; 2022 Apr; 13(1):2339. PubMed ID: 35487922
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computational solutions for spatial transcriptomics.
    Kleino I; Frolovaitė P; Suomi T; Elo LL
    Comput Struct Biotechnol J; 2022; 20():4870-4884. PubMed ID: 36147664
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology.
    Ding J; Li L; Lu Q; Venegas J; Wang Y; Wu L; Jin W; Wen H; Liu R; Tang W; Dai X; Li Z; Zuo W; Chang Y; Lei YL; Shang L; Danaher P; Xie Y; Tang J
    J Comput Biol; 2024 Sep; 31(9):871-885. PubMed ID: 39117342
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SpatialDeX is a Reference-Free Method for Cell Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors.
    Liu X; Tang G; Chen Y; Li Y; Li H; Wang X
    Cancer Res; 2024 Oct; ():. PubMed ID: 39387817
    [TBL] [Abstract][Full Text] [Related]  

  • 6. MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor.
    Peters Couto BZ; Robertson N; Patrick E; Ghazanfar S
    Bioinformatics; 2023 Sep; 39(9):. PubMed ID: 37698995
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.
    Thuilliez C; Moquin-Beaudry G; Khneisser P; Marques Da Costa ME; Karkar S; Boudhouche H; Drubay D; Audinot B; Geoerger B; Scoazec JY; Gaspar N; Marchais A
    Bioinform Adv; 2024; 4(1):vbae081. PubMed ID: 38915885
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data.
    Liu Y; Li N; Qi J; Xu G; Zhao J; Wang N; Huang X; Jiang W; Justet A; Adams TS; Homer R; Amei A; Rosas IO; Kaminski N; Wang Z; Yan X
    bioRxiv; 2023 Aug; ():. PubMed ID: 37662370
    [TBL] [Abstract][Full Text] [Related]  

  • 9. STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.
    Li Y; Luo Y
    Genome Biol; 2024 Aug; 25(1):206. PubMed ID: 39103939
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. ScType enables fast and accurate cell type identification from spatial transcriptomics data.
    Nader K; Tasci M; Ianevski A; Erickson A; Verschuren EW; Aittokallio T; Miihkinen M
    Bioinformatics; 2024 Jul; 40(7):. PubMed ID: 38936341
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks.
    Li Y; Luo Y
    bioRxiv; 2023 Jun; ():. PubMed ID: 37333198
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data.
    Zhang T; Zhang Z; Li L; Dong B; Wang G; Zhang D
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38127088
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve.
    Charytonowicz D; Brody R; Sebra R
    Nat Commun; 2023 Mar; 14(1):1350. PubMed ID: 36906603
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Comparative Methods for Demystifying Spatial Transcriptomics.
    Sammeth M; Mudra S; Bialdiga S; Hartmannsberger B; Kramer S; Rittner H
    Methods Mol Biol; 2024; 2802():515-546. PubMed ID: 38819570
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets.
    Maden SK; Kwon SH; Huuki-Myers LA; Collado-Torres L; Hicks SC; Maynard KR
    ArXiv; 2023 May; ():. PubMed ID: 37214135
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.