mouse hippocampus 10x visium hd data set  (10X Genomics)

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

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: Cell Reports Methods

Article Title: Differential expression analysis in single-cell and spatial RNA-seq without model assumptions

doi: 10.1016/j.crmeth.2026.101383

Figure Lengend Snippet: Weighted statistical testing reduces false-positive DGE in spRNA-seq (Visium HD assay) (A) Top: Proliferating chondrocytes were selected in 3 sections from the same E18.5 tibia; shaded areas show the selected regions in high resolution images of H&E-stained sections. Bottom: Zoomed images of the selected areas. Colored dots mark 8 × 8 μm 2 bins used for the analysis of gene expression, which passed quality control based on the following criteria: >20 features per bin, >100 counts other than Col1a1 or Col1a2 per bin, mitochondrial genes between 0.1% and 5% UMI, and Col2a1 > 5% UMI. The bin color represents the relative expression of Col2a1 ( n Col 2 a 1, i ×10,000). (B) DGE analysis performed with the weighted t test- χ 2 test combination and Seurat’s FindMarkers function for all 3 possible pairwise comparisons between the selected areas. Only genes detected in at least 10% of the bins were analyzed.

Article Snippet: For simplicity and consistency, we compare the results produced by our method with the standard models within the Seurat workflow, using publicly available 10× Genomics scRNA-seq datasets ( https://www.10xgenomics.com/datasets ) and our spRNA-seq data collected with the 10× Genomics Visium HD assay.

Techniques: HD Assay, Staining, Gene Expression, Control, Expressing

Journal: Cell Reports Methods

Article Title: Differential expression analysis in single-cell and spatial RNA-seq without model assumptions

doi: 10.1016/j.crmeth.2026.101383

Figure Lengend Snippet: Weighted statistical testing reduces false-positive DGE in spRNA-seq (Visium HD assay) (A) Top: Proliferating chondrocytes were selected in 3 sections from the same E18.5 tibia; shaded areas show the selected regions in high resolution images of H&E-stained sections. Bottom: Zoomed images of the selected areas. Colored dots mark 8 × 8 μm 2 bins used for the analysis of gene expression, which passed quality control based on the following criteria: >20 features per bin, >100 counts other than Col1a1 or Col1a2 per bin, mitochondrial genes between 0.1% and 5% UMI, and Col2a1 > 5% UMI. The bin color represents the relative expression of Col2a1 ( n Col 2 a 1, i ×10,000). (B) DGE analysis performed with the weighted t test- χ 2 test combination and Seurat’s FindMarkers function for all 3 possible pairwise comparisons between the selected areas. Only genes detected in at least 10% of the bins were analyzed.

Article Snippet: Three sections from the same tibia were placed on the same slide within 6.5 × 6.5 mm area, stained with Hematoxylin and Eosin, imaged on a Nikon Ti2-E inverted microscope with a 20X/0.8NA objective and analyzed with the Visium HD assay (10X Genomics) as described by the manufacturer.

Techniques: HD Assay, Staining, Gene Expression, Control, Expressing

Journal: Cell Reports Methods

Article Title: Differential expression analysis in single-cell and spatial RNA-seq without model assumptions

doi: 10.1016/j.crmeth.2026.101383

Figure Lengend Snippet: Weighted statistical testing reduces false-positive DGE in spRNA-seq (Visium HD assay) (A) Top: Proliferating chondrocytes were selected in 3 sections from the same E18.5 tibia; shaded areas show the selected regions in high resolution images of H&E-stained sections. Bottom: Zoomed images of the selected areas. Colored dots mark 8 × 8 μm 2 bins used for the analysis of gene expression, which passed quality control based on the following criteria: >20 features per bin, >100 counts other than Col1a1 or Col1a2 per bin, mitochondrial genes between 0.1% and 5% UMI, and Col2a1 > 5% UMI. The bin color represents the relative expression of Col2a1 ( n Col 2 a 1, i ×10,000). (B) DGE analysis performed with the weighted t test- χ 2 test combination and Seurat’s FindMarkers function for all 3 possible pairwise comparisons between the selected areas. Only genes detected in at least 10% of the bins were analyzed.

Article Snippet: Visium HD , 10X Genomics , cat #1000676.

Techniques: HD Assay, Staining, Gene Expression, Control, Expressing