10x genomics visium platform (10X Genomics)
86
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10X Genomics
10x genomics visium platform
10x Genomics Visium Platform, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/10x+visium+platform/cellranger/pmc13197639-320-7-7
Average 86 stars, based on 1 article reviews
10x Genomics Visium Platform, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/10x+visium+platform/cellranger/pmc13197639-320-7-7
Average 86 stars, based on 1 article reviews
10x genomics visium platform - by Bioz Stars,
2026-10
86/100 stars
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other:Article Title: A composite scaling network of EfficientNet for improving spatial domain identification performance. Article Snippet: The 10x Visium platform includes four data sets: the human dorsolateral prefrontal cortex (DLPFC) data set (http://research.libd.org/spatialLIBD/), along with data sets for human breast cancer, mouse brain sagittal anterior, and Article Title: A composite scaling network of EfficientNet for improving spatial domain identification performance Article Snippet: The 10x Visium platform includes four data sets: the human dorsolateral prefrontal cortex (DLPFC) data set ( http://research.libd.org/spatialLIBD/ ), along with data sets for human breast cancer, mouse brain sagittal anterior, and Spatial Transcriptomics:Article Title: GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis. Article Snippet: .. Data Availability Statement The spatial transcriptomics datasets analyzed in this study are publicly available from the following sources: the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset, which was obtained using the Article Title: GatorST: A Versatile Contrastive Meta‐Learning Framework for Spatial Transcriptomic Data Analysis Article Snippet: .. The spatial transcriptomics datasets analyzed in this study are publicly available from the following sources: the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset, which was obtained using the Article Title: Techniques and analytic workflow for spatial transcriptomics and its application to allergy and inflammation. Article Snippet: Abbreviations used COPD: Chronic obstructive pulmonary disease DEG: Differentially expressed gene FOV: Field of view PC: Principal component ROI: Region of interest scRNA-seq: Single-cell RNA sequencing SVG: Spatially variable gene UMAP: Uniform Manifold Approximation and Projection Spatial profiling, through single-cell gene-level expression data paired with cell localization, offers unprecedented biologic insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation.. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biologic interpretation of data.. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions that are unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis and T-cell interactions in the lung in chronic obstructive pulmonary disease. Blocking Assay:Article Title: GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis. Article Snippet: .. Data Availability Statement The spatial transcriptomics datasets analyzed in this study are publicly available from the following sources: the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset, which was obtained using the RNA Sequencing:Article Title: Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics. Article Snippet: Hao Chi, Department of Clinical Medicine, Southwest Medical University, Luzhou 646000, China.. Email: chihao7511@163.com Abstract Background: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates.. A key focus is glutamine metabolism, governed by select genes. Generated:Article Title: MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance Article Snippet: .. These adult mouse brain tissue sections are sequenced by the Transcriptomics:Article Title: MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance Article Snippet: .. These adult mouse brain tissue sections are sequenced by the Amplification:Article Title: Techniques and analytic workflow for spatial transcriptomics and its application to allergy and inflammation. Article Snippet: Abbreviations used COPD: Chronic obstructive pulmonary disease DEG: Differentially expressed gene FOV: Field of view PC: Principal component ROI: Region of interest scRNA-seq: Single-cell RNA sequencing SVG: Spatially variable gene UMAP: Uniform Manifold Approximation and Projection Spatial profiling, through single-cell gene-level expression data paired with cell localization, offers unprecedented biologic insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation.. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biologic interpretation of data.. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions that are unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis and T-cell interactions in the lung in chronic obstructive pulmonary disease. Sequencing:Article Title: Techniques and analytic workflow for spatial transcriptomics and its application to allergy and inflammation. Article Snippet: Abbreviations used COPD: Chronic obstructive pulmonary disease DEG: Differentially expressed gene FOV: Field of view PC: Principal component ROI: Region of interest scRNA-seq: Single-cell RNA sequencing SVG: Spatially variable gene UMAP: Uniform Manifold Approximation and Projection Spatial profiling, through single-cell gene-level expression data paired with cell localization, offers unprecedented biologic insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation.. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biologic interpretation of data.. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions that are unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis and T-cell interactions in the lung in chronic obstructive pulmonary disease. |