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human her2-positive breast tumor (her2+) dataset  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc human her2-positive breast tumor (her2+) dataset
    GRAS4T accurately identified spatial domains in the <t>HER2+</t> dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.
    Human Her2 Positive Breast Tumor (Her2+) Dataset, supplied by Spatial Transcriptomics Inc, 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+her2-positive+breast+tumor+(her2+)+dataset/her2++breast+cancer+data/pmc11539238-169-9-21
    Average 90 stars, based on 1 article reviews
    human her2-positive breast tumor (her2+) dataset - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning"

    Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

    Journal: Computational and Structural Biotechnology Journal

    doi: 10.1016/j.csbj.2024.10.029

    GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.
    Figure Legend Snippet: GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.

    Techniques Used:

    Related Articles

    Expressing:

    Article Title: Deciphering Spatial Domains by Integrating Histopathological Image and Tran-scriptomics via Contrastive Learning
    Article Snippet: To evaluate the performance of our method, we employed four spatial transcriptomics datasets, including the human HER2-positive breast tumor dataset (HER2+) [ ], the human dorsolateral prefrontal cortex dataset (spatialLIBD) [ ], the human epidermal growth factor receptor (HER) 2-amplified (HER+) invasive ductal carcinoma (IDC) sample ( https://sup-port.10xgenomics.com/spatial-gene-expression/datasets ), and the mouse brain datasets ( https://www.10xgenomics.com/resources/datasets ).

    Article Title: A contrastive learning approach to integrate spatial transcriptomics and histological images
    Article Snippet: Additionally, our models can be effectively used for the HER2-positive breast tumor dataset of spatial transcriptomics technology and the human lung dataset of CosMx SMI technology.

    Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning
    Article Snippet: The efficacy of GRAS4T was further evaluated using the human HER2-positive breast tumor (HER2+) dataset , which used different spatial technologies (spatial transcriptomics) compared to the DLPFC dataset.

    Generated:

    Article Title: Deciphering Spatial Domains by Integrating Histopathological Image and Tran-scriptomics via Contrastive Learning
    Article Snippet: To evaluate the performance of our method, we employed four spatial transcriptomics datasets, including the human HER2-positive breast tumor dataset (HER2+) [ ], the human dorsolateral prefrontal cortex dataset (spatialLIBD) [ ], the human epidermal growth factor receptor (HER) 2-amplified (HER+) invasive ductal carcinoma (IDC) sample ( https://sup-port.10xgenomics.com/spatial-gene-expression/datasets ), and the mouse brain datasets ( https://www.10xgenomics.com/resources/datasets ).

    Article Title: A contrastive learning approach to integrate spatial transcriptomics and histological images
    Article Snippet: Additionally, our models can be effectively used for the HER2-positive breast tumor dataset of spatial transcriptomics technology and the human lung dataset of CosMx SMI technology.

    Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning
    Article Snippet: The efficacy of GRAS4T was further evaluated using the human HER2-positive breast tumor (HER2+) dataset , which used different spatial technologies (spatial transcriptomics) compared to the DLPFC dataset.



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    human her2-positive breast tumor (her2+) dataset  (Spatial Transcriptomics Inc)
    90
    Spatial Transcriptomics Inc human her2-positive breast tumor (her2+) dataset
    GRAS4T accurately identified spatial domains in the <t>HER2+</t> dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.
    Human Her2 Positive Breast Tumor (Her2+) Dataset, supplied by Spatial Transcriptomics Inc, 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+her2-positive+breast+tumor+(her2+)+dataset/her2++breast+cancer+data/pmc11539238-169-9-21
    Average 90 stars, based on 1 article reviews
    human her2-positive breast tumor (her2+) dataset - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    human her2-positive breast tumor dataset  (Spatial Transcriptomics Inc)
    90
    Spatial Transcriptomics Inc human her2-positive breast tumor dataset
    Spatial domains and SVGs detected in the human <t>HER2-positive</t> breast tumor (HER2+) dataset and the invasive ductal carcinoma (IDC) dataset. (a) Boxplot of clustering accuracy in all sections of the HER2+ dataset in terms of ARI values for all methods. (b) Manually annotated regions of section E1 of the HER2+ dataset, spatial domains detected by Giotto and ConGI. (c) The histopathological image and manually annotated regions for the IDC data, spatial domains detected by STAGATE, SpaGCN, conST, BayesSpace, and ConGI, and the spatial expression patterns of SVGs for ConGI predicted spatial domains 0 (FAM234B) and 3 (MUC1).
    Human Her2 Positive Breast Tumor Dataset, supplied by Spatial Transcriptomics Inc, 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+her2-positive+breast+tumor+(her2+)+dataset/her2++breast+cancer+data/bio_rxiv__2022__09__30__510297-40-15-10
    Average 90 stars, based on 1 article reviews
    human her2-positive breast tumor dataset - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.

    Journal: Computational and Structural Biotechnology Journal

    Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

    doi: 10.1016/j.csbj.2024.10.029

    Figure Lengend Snippet: GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.

    Article Snippet: The efficacy of GRAS4T was further evaluated using the human HER2-positive breast tumor (HER2+) dataset , which used different spatial technologies (spatial transcriptomics) compared to the DLPFC dataset.

    Techniques:

    Spatial domains and SVGs detected in the human HER2-positive breast tumor (HER2+) dataset and the invasive ductal carcinoma (IDC) dataset. (a) Boxplot of clustering accuracy in all sections of the HER2+ dataset in terms of ARI values for all methods. (b) Manually annotated regions of section E1 of the HER2+ dataset, spatial domains detected by Giotto and ConGI. (c) The histopathological image and manually annotated regions for the IDC data, spatial domains detected by STAGATE, SpaGCN, conST, BayesSpace, and ConGI, and the spatial expression patterns of SVGs for ConGI predicted spatial domains 0 (FAM234B) and 3 (MUC1).

    Journal: bioRxiv

    Article Title: Deciphering Spatial Domains by Integrating Histopathological Image and Tran-scriptomics via Contrastive Learning

    doi: 10.1101/2022.09.30.510297

    Figure Lengend Snippet: Spatial domains and SVGs detected in the human HER2-positive breast tumor (HER2+) dataset and the invasive ductal carcinoma (IDC) dataset. (a) Boxplot of clustering accuracy in all sections of the HER2+ dataset in terms of ARI values for all methods. (b) Manually annotated regions of section E1 of the HER2+ dataset, spatial domains detected by Giotto and ConGI. (c) The histopathological image and manually annotated regions for the IDC data, spatial domains detected by STAGATE, SpaGCN, conST, BayesSpace, and ConGI, and the spatial expression patterns of SVGs for ConGI predicted spatial domains 0 (FAM234B) and 3 (MUC1).

    Article Snippet: To evaluate the performance of our method, we employed four spatial transcriptomics datasets, including the human HER2-positive breast tumor dataset (HER2+) [ ], the human dorsolateral prefrontal cortex dataset (spatialLIBD) [ ], the human epidermal growth factor receptor (HER) 2-amplified (HER+) invasive ductal carcinoma (IDC) sample ( https://sup-port.10xgenomics.com/spatial-gene-expression/datasets ), and the mouse brain datasets ( https://www.10xgenomics.com/resources/datasets ).

    Techniques: Expressing

    UMAP visualizations and PAGA graphs using representations generated by STAGATE, BayesSpace, and ConGI respectively (a) in the section E1 of the dataset HER2+ and (b) the section 151509 of the dataset spatialLIBD.

    Journal: bioRxiv

    Article Title: Deciphering Spatial Domains by Integrating Histopathological Image and Tran-scriptomics via Contrastive Learning

    doi: 10.1101/2022.09.30.510297

    Figure Lengend Snippet: UMAP visualizations and PAGA graphs using representations generated by STAGATE, BayesSpace, and ConGI respectively (a) in the section E1 of the dataset HER2+ and (b) the section 151509 of the dataset spatialLIBD.

    Article Snippet: To evaluate the performance of our method, we employed four spatial transcriptomics datasets, including the human HER2-positive breast tumor dataset (HER2+) [ ], the human dorsolateral prefrontal cortex dataset (spatialLIBD) [ ], the human epidermal growth factor receptor (HER) 2-amplified (HER+) invasive ductal carcinoma (IDC) sample ( https://sup-port.10xgenomics.com/spatial-gene-expression/datasets ), and the mouse brain datasets ( https://www.10xgenomics.com/resources/datasets ).

    Techniques: Generated

    Ablation study for ConGI on the HER2+ dataset measured by ARI.

    Journal: bioRxiv

    Article Title: Deciphering Spatial Domains by Integrating Histopathological Image and Tran-scriptomics via Contrastive Learning

    doi: 10.1101/2022.09.30.510297

    Figure Lengend Snippet: Ablation study for ConGI on the HER2+ dataset measured by ARI.

    Article Snippet: To evaluate the performance of our method, we employed four spatial transcriptomics datasets, including the human HER2-positive breast tumor dataset (HER2+) [ ], the human dorsolateral prefrontal cortex dataset (spatialLIBD) [ ], the human epidermal growth factor receptor (HER) 2-amplified (HER+) invasive ductal carcinoma (IDC) sample ( https://sup-port.10xgenomics.com/spatial-gene-expression/datasets ), and the mouse brain datasets ( https://www.10xgenomics.com/resources/datasets ).

    Techniques: