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mouse brain visium hd dataset  (10X Genomics)

 
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    Structured Review

    10X Genomics mouse brain visium hd dataset
    Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
    Mouse Brain Visium Hd Dataset, 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/mouse+brain+visium+hd+dataset/pmc13069690-275-0-5?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    mouse brain visium hd dataset - by Bioz Stars, 2026-08
    86/100 stars

    Images

    1) Product Images from "SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics"

    Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

    Journal: NAR Genomics and Bioinformatics

    doi: 10.1093/nargab/lqag039

    Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
    Figure Legend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

    Techniques Used: Expressing, Extraction, Construct, Quantitative Proteomics



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    mouse brain visium hd dataset  (10X Genomics)
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    10X Genomics mouse brain visium hd dataset
    Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
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    10x visium hd mouse brain dataset  (10X Genomics)
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    10X Genomics 10x visium hd mouse brain dataset
    Spatial density analysis of <t>Visium</t> HD mouse brain data via scider. (A) Spatial plot with cell type annotation. (B) Heatmap of spatial density of cortex layer 2/3 and 6, dentate gyrus, hippocampus, immune infiltrates and oligodendrocytes. Grids of densities less than the median are filtered out in this visualization. (C) Spatial plot of hippocampus cells, overlaid by contour lines calculated from the spatial density of hippocampus. (D) Cell type composition at each contour level of hippocampus density cross the whole slide. (E) MA plots for gene expression changes associated with hippocampus densities. (F) Top neuro-associated genesets enriched in the differential expressed genes in E.
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    Image Search Results


    Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

    Journal: NAR Genomics and Bioinformatics

    Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

    doi: 10.1093/nargab/lqag039

    Figure Lengend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

    Article Snippet: Mouse brain Visium HD dataset: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen .

    Techniques: Expressing, Extraction, Construct, Quantitative Proteomics

    (a) sST: Visium HD mouse brain, grid expression over H&E. (b) iST: Xenium human breast cancer; DAPI/IF morphology (left) and cluster-colored centroids (right). (c) SP: CODEX human intestine with protein-defined clusters. (d) scRNA-seq: honey bee brain, 3D UMAP. (e, f) Lasso-defined inner (e) and large (f) Kenyon cell (KC) ROIs (left); linked embedding confirms molecular coherence (right). (g) Differential expression between inner and large KCs (left: Dop3 -colored spatial view; right: DEG heatmap). (h) Spatially varying gene CHIT1 expression: whole tissue (left), ROI1 (middle), ROI2 (right). (i) Same layout as h, CD83 . (j) Spatially resolved ROI1 cell-type clusters (left) and TAMs (cluster 11) sub-clusters (right). (k) Spatially resolved ROI2 cell-type clusters. (l) Cell type composition of ROI1 and ROI2. (m) Volcano of ROI1-core-specific TAMs (11.1) vs other TAMs (11.0 and 11.2).

    Journal: bioRxiv

    Article Title: MilliMap: interactive closed-loop analysis for spatial omics

    doi: 10.64898/2026.05.01.722104

    Figure Lengend Snippet: (a) sST: Visium HD mouse brain, grid expression over H&E. (b) iST: Xenium human breast cancer; DAPI/IF morphology (left) and cluster-colored centroids (right). (c) SP: CODEX human intestine with protein-defined clusters. (d) scRNA-seq: honey bee brain, 3D UMAP. (e, f) Lasso-defined inner (e) and large (f) Kenyon cell (KC) ROIs (left); linked embedding confirms molecular coherence (right). (g) Differential expression between inner and large KCs (left: Dop3 -colored spatial view; right: DEG heatmap). (h) Spatially varying gene CHIT1 expression: whole tissue (left), ROI1 (middle), ROI2 (right). (i) Same layout as h, CD83 . (j) Spatially resolved ROI1 cell-type clusters (left) and TAMs (cluster 11) sub-clusters (right). (k) Spatially resolved ROI2 cell-type clusters. (l) Cell type composition of ROI1 and ROI2. (m) Volcano of ROI1-core-specific TAMs (11.1) vs other TAMs (11.0 and 11.2).

    Article Snippet: The Visium HD Mouse Brain dataset (FFPE; C57BL/6; Space Ranger v3.0.0) is available from 10x Genomics at https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he , licensed under CC BY 4.0.

    Techniques: Expressing, Quantitative Proteomics

    Spatial density analysis of Visium HD mouse brain data via scider. (A) Spatial plot with cell type annotation. (B) Heatmap of spatial density of cortex layer 2/3 and 6, dentate gyrus, hippocampus, immune infiltrates and oligodendrocytes. Grids of densities less than the median are filtered out in this visualization. (C) Spatial plot of hippocampus cells, overlaid by contour lines calculated from the spatial density of hippocampus. (D) Cell type composition at each contour level of hippocampus density cross the whole slide. (E) MA plots for gene expression changes associated with hippocampus densities. (F) Top neuro-associated genesets enriched in the differential expressed genes in E.

    Journal: bioRxiv

    Article Title: Preserving tissue structure through density-based spatial analysis with scider

    doi: 10.1101/2025.09.11.675745

    Figure Lengend Snippet: Spatial density analysis of Visium HD mouse brain data via scider. (A) Spatial plot with cell type annotation. (B) Heatmap of spatial density of cortex layer 2/3 and 6, dentate gyrus, hippocampus, immune infiltrates and oligodendrocytes. Grids of densities less than the median are filtered out in this visualization. (C) Spatial plot of hippocampus cells, overlaid by contour lines calculated from the spatial density of hippocampus. (D) Cell type composition at each contour level of hippocampus density cross the whole slide. (E) MA plots for gene expression changes associated with hippocampus densities. (F) Top neuro-associated genesets enriched in the differential expressed genes in E.

    Article Snippet: The 10x Visium HD mouse brain dataset was obtained from the official 10x Genomics repository ( https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brainhe ).

    Techniques: Gene Expression