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human lymph node dataset 10x visium  (10X Genomics)

 
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    10X Genomics human lymph node dataset 10x visium
    DeepGFT effectively characterized the subtle tissue architectures of <t>human</t> <t>lymph</t> <t>node</t> Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1
    Human Lymph Node Dataset 10x Visium, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+lymph+node+visium+dataset/10x+visium+dataset/pmc12135315-388-1-11
    Average 90 stars, based on 1 article reviews
    human lymph node dataset 10x visium - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform"

    Article Title: DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform

    Journal: Genome Biology

    doi: 10.1186/s13059-025-03631-5

    DeepGFT effectively characterized the subtle tissue architectures of human lymph node Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1
    Figure Legend Snippet: DeepGFT effectively characterized the subtle tissue architectures of human lymph node Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1

    Techniques Used: Generated

    Related Articles

    other:

    Article Title: The Landscape of Tumor Cell States and Ecosystems in Diffuse Large B Cell Lymphoma
    Article Snippet: Human lymph node Visium dataset (Space Ranger 1.0.0) , 10x Genomics , https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Human_Lymph_Node.



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    Image Search Results


    DeepGFT effectively characterized the subtle tissue architectures of human lymph node Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1

    Journal: Genome Biology

    Article Title: DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform

    doi: 10.1186/s13059-025-03631-5

    Figure Lengend Snippet: DeepGFT effectively characterized the subtle tissue architectures of human lymph node Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1

    Article Snippet: The human lymph node dataset (10x Visium) is available at ( https://www.10xgenomics.com/cn/resources/datasets/human-lymph-node-1-standard-1-1-0 ) [ ].

    Techniques: Generated