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10X Genomics
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Roboz Surgical
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10X Genomics
xenium 2d mouse brain dataset Xenium 2d Mouse Brain 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/bio_rxiv__64898__2026__06__03__729557-323-5-15?v=10X+Genomics Average 86 stars, based on 1 article reviews
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10X Genomics
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10X Genomics
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2026-07
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Zivic Laboratories Inc
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10X Genomics
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2026-07
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10X Genomics
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10X Genomics
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10X Genomics
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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: SpNeigh reveals intermediate cell populations near boundaries in mouse brain Xenium data. ( a ) Spatial plots showing different annotation types. Left: Cells colored by clusters with overlaid boundaries of cluster 2. Middle: Manual cluster-level annotations based on brain anatomy. Right: Reference-based single-cell annotations, with selected subtypes merged. CGE: caudal ganglionic eminence; MGE: medial ganglionic eminence. ( b ) Neighborhood analysis of cluster 2. Top: Boundary and ring regions. Bottom: Cells within boundary and ring regions for region 1, with donut plots showing cluster proportions (labels shown for proportions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge$\end{document} 5%). ( c ) Expression of Slc17a7 and Sox10 in cluster 2 cells inside boundaries and surrounding rings. Slc17a7, a marker of cortical excitatory neurons, shows elevated expression in outer cells near the boundary. Sox10 is broadly expressed in oligodendrocytes and remains consistent across both inner and outer cells in cluster 2. ( d ) Boundary 1 of cluster 2 split into discrete edges. ( e ) Spatial weights relative to edge 2 for cortical cells. Black line indicates edge 2. ( f ) Top spatially varying genes identified by RunSpatialDE using weights from edge 2. ( g ) Expression of Ccn2 and Cplx3 near edge 2. Cells include cortical layer 4/5/6 neurons, L6b neurons, astrocytes, and oligodendrocytes. L6b cells are localized along edge 2.
Article Snippet:
Techniques: Single Cell, Expressing, Marker
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:
Techniques: Expressing, Extraction, Construct, Quantitative Proteomics
Journal: Cell Reports Methods
Article Title: Multiscale domain identification for spatial transcriptomics via persistent homology
doi: 10.1016/j.crmeth.2026.101376
Figure Lengend Snippet: PHD-MS identifies stable brain structures in a Visium mouse brain (A) An annotated coronal section from Allen Brain Atlas as the ground truth. (B) NeST coexpression hotspots of the Visium mouse brain data. NeST identifies 5 relevant layers of structure, color coded by layer (bottom), matched to the annotated regions of Allen Atlas (right). (C) Nested PHD-MS domains with layer labels, color coded with matched regions in Allen Atlas. Top layer represents the domain at its broadest scale, including low-coreness spots. Middle layer includes medium-coreness spots, and core layer forms the domain’s stable core.
Article Snippet:
Techniques: