10x visium dataset  (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: 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

Journal: Nature Communications

Article Title: Charting spatial ligand-target activity using Renoir

doi: 10.1038/s41467-026-72388-7

Figure Lengend Snippet: a Preparation of FFPE fetal liver tissue. Created in BioRender. Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1). Black scale bar, 1 mm. c Distribution of UMI count across the tissue section. d Spatial map of communication domains and neighborhood activity scores for differentially active ligand-target pairs ( COL18A1:BCL6 , PLG:MARCO , and MDK:KLF6 ). e Ranking of ligands based on their cumulative activities over target genes expressed by major cell types in domain 0. f UMAP plot of latent embedding for hepatocyte population in scRNA-seq data, cells are colored as either PLG + or PLG − . g Volcano plot depicting differentially expressed genes between PLG + and PLG − Hepatocytes calculated using non-parametric Wilcoxon rank sum test ( − log 10 P threshold = 50 and log 2 foldchange threshold = 0.3). h Spatial feature plot of PLG expression and cell type abundances of Hepatocytes ( PLG + and PLG − ) and FOLR2 + Macrophages overlaid onto the spots in ST data. i Spatial similarity measures between PLG:MARCO neighborhood activity scores and cell type abundances of PLG + and PLG − Hepatocytes across neighborhoods containing FOLR2 + Macrophages ( n = 39651 spots). Source data are provided as a Source Data file.

Article Snippet: Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1).

Techniques: Activity Assay, Expressing