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visium hd mouse small intestine ffpe dataset (10X Genomics)

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10X Genomics visium hd mouse small intestine ffpe dataset

(a) Abundance invariance test. Ranking stability of oligodendrocyte markers (top 20 genes by expression in oligodendrocytes) in mouse brain scRNA-seq (31,053 genes) as cell population is downsampled from 26.7% to 0.4%. Rank denotes average position when all genes are sorted by score (rank 1 = highest). Variance-based ranking (blue) degrades from rank 115 to 240 as abundance decreases—a two-fold deterioration. Leverage-score ranking (red) remains stable at rank ~ 150 regardless of population size, demonstrating true decoupling of biological identity from numerical prevalence. (b) The variance-leverage plane. Classification of 31,053 genes by variance (x-axis) and leverage score (y-axis). Four quadrants emerge: structurally informative “GOLD” genes (green, low variance/high leverage) include vascular markers ( Cldn5, Rgs5, Ly6a, Abcb1a, Hspb1 ) that define rare anatomical structures; variance-dominated “NOISE” genes (red, high variance/low leverage) contain 35% unannotated Gm -series transcripts compared to only 6% in the GOLD set, indicating that high variance alone does not ensure cell-type discriminative power. (c) Functional enrichment analysis. GO Biological Process enrichment reveals GOLD genes are significantly enriched for regulation of angiogenesis (FDR-adjusted p = 2.8 × 10 −6 ), endothelial cell differentiation (FDR-adjusted p = 2.1 × 10 −4 ), vasculogenesis, and blood vessel morphogenesis. NOISE genes show zero significant GO terms at FDR-adjusted p < 0.05. Genome-wide cell type specificity analysis further confirms that GOLD genes systematically target rare populations (median 0.27% abundance) versus NOISE genes (0.51%; p = 3.25 × 10 −25 ), with Endothelial cells as the top target—validating leverage as an unsupervised metric for biological distinctiveness. (d) Spatial verification on <t>Visium.</t> Top row: GOLD genes ( Cldn5, Ly6a, Rgs5 ) reconstruct clear vascular anatomical structures on mouse brain Visium sections (spatial structure score = 1.33). Bottom row: NOISE genes exhibit random, speckle-like distribution patterns (structure score = 0.87; Mann-Whitney p = 5.6 × 10 −5 ). This visual contrast demonstrates that leverage selects for genuine biological structure rather than technical variation.

Visium Hd Mouse Small Intestine Ffpe 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/result/visium hd mouse small intestine ffpe dataset/product/10X Genomics
Average 86 stars, based on 1 article reviews

visium hd mouse small intestine ffpe dataset - by Bioz Stars, 2026-05

86/100 stars

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1) Product Images from "FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching"

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

Journal: bioRxiv

doi: 10.64898/2025.12.22.696108

Figure Legend Snippet: (a) Abundance invariance test. Ranking stability of oligodendrocyte markers (top 20 genes by expression in oligodendrocytes) in mouse brain scRNA-seq (31,053 genes) as cell population is downsampled from 26.7% to 0.4%. Rank denotes average position when all genes are sorted by score (rank 1 = highest). Variance-based ranking (blue) degrades from rank 115 to 240 as abundance decreases—a two-fold deterioration. Leverage-score ranking (red) remains stable at rank ~ 150 regardless of population size, demonstrating true decoupling of biological identity from numerical prevalence. (b) The variance-leverage plane. Classification of 31,053 genes by variance (x-axis) and leverage score (y-axis). Four quadrants emerge: structurally informative “GOLD” genes (green, low variance/high leverage) include vascular markers ( Cldn5, Rgs5, Ly6a, Abcb1a, Hspb1 ) that define rare anatomical structures; variance-dominated “NOISE” genes (red, high variance/low leverage) contain 35% unannotated Gm -series transcripts compared to only 6% in the GOLD set, indicating that high variance alone does not ensure cell-type discriminative power. (c) Functional enrichment analysis. GO Biological Process enrichment reveals GOLD genes are significantly enriched for regulation of angiogenesis (FDR-adjusted p = 2.8 × 10 −6 ), endothelial cell differentiation (FDR-adjusted p = 2.1 × 10 −4 ), vasculogenesis, and blood vessel morphogenesis. NOISE genes show zero significant GO terms at FDR-adjusted p < 0.05. Genome-wide cell type specificity analysis further confirms that GOLD genes systematically target rare populations (median 0.27% abundance) versus NOISE genes (0.51%; p = 3.25 × 10 −25 ), with Endothelial cells as the top target—validating leverage as an unsupervised metric for biological distinctiveness. (d) Spatial verification on Visium. Top row: GOLD genes ( Cldn5, Ly6a, Rgs5 ) reconstruct clear vascular anatomical structures on mouse brain Visium sections (spatial structure score = 1.33). Bottom row: NOISE genes exhibit random, speckle-like distribution patterns (structure score = 0.87; Mann-Whitney p = 5.6 × 10 −5 ). This visual contrast demonstrates that leverage selects for genuine biological structure rather than technical variation.

Techniques Used: Expressing, Functional Assay, Cell Differentiation, Genome Wide, MANN-WHITNEY

$(a) FlashDeconv performance across resolutions. Left: Number of measurement units at each bin size. Middle: Processing time demonstrating scalability to 350,000+ spots. Right: Signal purity (fraction of spots with > 80% single cell type) collapses from 61.5% at 8 µ m to 13.3% at 16 µ m. (b) Spatial maps of enterocyte proportions at 8, 16, and 32 µ m resolution on mouse small intestine. Fine anatomical detail visible at 8 µ m becomes progressively blurred at coarser resolutions. (c) Resolution sensitivity varies by cell type. Stem cells (red) show the steepest decline in spatial coherence (Moran’s I), while Paneth cells (blue) retain spatial structure. Shaded region indicates the 8–16 µ m transition zone. (d) Cryptvillus boundary validation. Gradient sharpness decreases by 77% from 8 µ m to 16 µ m, quantifying anatomical blurring. (e) Spatial binning induces spurious colocalization. Paneth and Goblet cells show weak mutual exclusion at 8 µ m ( r = −0.12, p < 10 −100 ) but appear strongly colocalized at 64 µ m ( r = +0.80, p < 10 −100 )—a correlation sign flip that could lead to incorrect biological conclusions about cell-cell interactions. Data: Visium HD Mouse Small Intestine (10x Genomics), scRNA-seq reference from Haber et al. 2017.$
Figure Legend Snippet: (a) FlashDeconv performance across resolutions. Left: Number of measurement units at each bin size. Middle: Processing time demonstrating scalability to 350,000+ spots. Right: Signal purity (fraction of spots with > 80% single cell type) collapses from 61.5% at 8 µ m to 13.3% at 16 µ m. (b) Spatial maps of enterocyte proportions at 8, 16, and 32 µ m resolution on mouse small intestine. Fine anatomical detail visible at 8 µ m becomes progressively blurred at coarser resolutions. (c) Resolution sensitivity varies by cell type. Stem cells (red) show the steepest decline in spatial coherence (Moran’s I), while Paneth cells (blue) retain spatial structure. Shaded region indicates the 8–16 µ m transition zone. (d) Cryptvillus boundary validation. Gradient sharpness decreases by 77% from 8 µ m to 16 µ m, quantifying anatomical blurring. (e) Spatial binning induces spurious colocalization. Paneth and Goblet cells show weak mutual exclusion at 8 µ m ( r = −0.12, p < 10 −100 ) but appear strongly colocalized at 64 µ m ( r = +0.80, p < 10 −100 )—a correlation sign flip that could lead to incorrect biological conclusions about cell-cell interactions. Data: Visium HD Mouse Small Intestine (10x Genomics), scRNA-seq reference from Haber et al. 2017.

Techniques Used: Biomarker Discovery

(a) HVG blindness ranking across intestinal cell types. HVG blindness is defined as the difference in mean percentile rank of a cell type’s marker genes under variance-based versus leverage-based selection; positive values indicate systematic underweighting by HVG. Tuft (brush) cells exhibit the highest HVG blindness (21 percentile points). (b) Spatial distribution of Tuft cells at 8 µ m resolution reveals focal niches (red spots) with proportions up to 61%. (c) Stem cell distribution at 8 µ m shows concentration at crypt bases. (d) Resolution sensitivity of Tuft cell detection. Maximum proportion decreases from 61% (8 µ m) to 4% (128 µ m), rendering focal niches undetectable at conventional resolution. (e) Colocalization analysis reveals Tuft cell hotspots are enriched 16.8-fold for stem cells and 15.3-fold for enteroendocrine cells ( p < 10 −4 , permutation test), but depleted for differentiated cell types (enterocytes 0.11×, goblet cells 0.10×). (f) Spatial zoom showing Tuft-Stem co-localization at crypt bases (blue: Stem-high, pink: co-localized). Tuft cells rarely appear without adjacent stem cells, consistent with their intimate niche association. Data: Visium HD Mouse Small Intestine (10x Genomics).

Figure Legend Snippet: (a) HVG blindness ranking across intestinal cell types. HVG blindness is defined as the difference in mean percentile rank of a cell type’s marker genes under variance-based versus leverage-based selection; positive values indicate systematic underweighting by HVG. Tuft (brush) cells exhibit the highest HVG blindness (21 percentile points). (b) Spatial distribution of Tuft cells at 8 µ m resolution reveals focal niches (red spots) with proportions up to 61%. (c) Stem cell distribution at 8 µ m shows concentration at crypt bases. (d) Resolution sensitivity of Tuft cell detection. Maximum proportion decreases from 61% (8 µ m) to 4% (128 µ m), rendering focal niches undetectable at conventional resolution. (e) Colocalization analysis reveals Tuft cell hotspots are enriched 16.8-fold for stem cells and 15.3-fold for enteroendocrine cells ( p < 10 −4 , permutation test), but depleted for differentiated cell types (enterocytes 0.11×, goblet cells 0.10×). (f) Spatial zoom showing Tuft-Stem co-localization at crypt bases (blue: Stem-high, pink: co-localized). Tuft cells rarely appear without adjacent stem cells, consistent with their intimate niche association. Data: Visium HD Mouse Small Intestine (10x Genomics).

Techniques Used: Marker, Selection, Concentration Assay

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Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) Abundance invariance test. Ranking stability of oligodendrocyte markers (top 20 genes by expression in oligodendrocytes) in mouse brain scRNA-seq (31,053 genes) as cell population is downsampled from 26.7% to 0.4%. Rank denotes average position when all genes are sorted by score (rank 1 = highest). Variance-based ranking (blue) degrades from rank 115 to 240 as abundance decreases—a two-fold deterioration. Leverage-score ranking (red) remains stable at rank ~ 150 regardless of population size, demonstrating true decoupling of biological identity from numerical prevalence. (b) The variance-leverage plane. Classification of 31,053 genes by variance (x-axis) and leverage score (y-axis). Four quadrants emerge: structurally informative “GOLD” genes (green, low variance/high leverage) include vascular markers ( Cldn5, Rgs5, Ly6a, Abcb1a, Hspb1 ) that define rare anatomical structures; variance-dominated “NOISE” genes (red, high variance/low leverage) contain 35% unannotated Gm -series transcripts compared to only 6% in the GOLD set, indicating that high variance alone does not ensure cell-type discriminative power. (c) Functional enrichment analysis. GO Biological Process enrichment reveals GOLD genes are significantly enriched for regulation of angiogenesis (FDR-adjusted p = 2.8 × 10 −6 ), endothelial cell differentiation (FDR-adjusted p = 2.1 × 10 −4 ), vasculogenesis, and blood vessel morphogenesis. NOISE genes show zero significant GO terms at FDR-adjusted p < 0.05. Genome-wide cell type specificity analysis further confirms that GOLD genes systematically target rare populations (median 0.27% abundance) versus NOISE genes (0.51%; p = 3.25 × 10 −25 ), with Endothelial cells as the top target—validating leverage as an unsupervised metric for biological distinctiveness. (d) Spatial verification on Visium. Top row: GOLD genes ( Cldn5, Ly6a, Rgs5 ) reconstruct clear vascular anatomical structures on mouse brain Visium sections (spatial structure score = 1.33). Bottom row: NOISE genes exhibit random, speckle-like distribution patterns (structure score = 0.87; Mann-Whitney p = 5.6 × 10 −5 ). This visual contrast demonstrates that leverage selects for genuine biological structure rather than technical variation.

Article Snippet: The Visium HD Mouse Small Intestine (FFPE) dataset was obtained from 10x Genomics ( https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine ).

Techniques: Expressing, Functional Assay, Cell Differentiation, Genome Wide, MANN-WHITNEY

Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) FlashDeconv performance across resolutions. Left: Number of measurement units at each bin size. Middle: Processing time demonstrating scalability to 350,000+ spots. Right: Signal purity (fraction of spots with > 80% single cell type) collapses from 61.5% at 8 µ m to 13.3% at 16 µ m. (b) Spatial maps of enterocyte proportions at 8, 16, and 32 µ m resolution on mouse small intestine. Fine anatomical detail visible at 8 µ m becomes progressively blurred at coarser resolutions. (c) Resolution sensitivity varies by cell type. Stem cells (red) show the steepest decline in spatial coherence (Moran’s I), while Paneth cells (blue) retain spatial structure. Shaded region indicates the 8–16 µ m transition zone. (d) Cryptvillus boundary validation. Gradient sharpness decreases by 77% from 8 µ m to 16 µ m, quantifying anatomical blurring. (e) Spatial binning induces spurious colocalization. Paneth and Goblet cells show weak mutual exclusion at 8 µ m ( r = −0.12, p < 10 −100 ) but appear strongly colocalized at 64 µ m ( r = +0.80, p < 10 −100 )—a correlation sign flip that could lead to incorrect biological conclusions about cell-cell interactions. Data: Visium HD Mouse Small Intestine (10x Genomics), scRNA-seq reference from Haber et al. 2017.

Techniques: Biomarker Discovery

Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) HVG blindness ranking across intestinal cell types. HVG blindness is defined as the difference in mean percentile rank of a cell type’s marker genes under variance-based versus leverage-based selection; positive values indicate systematic underweighting by HVG. Tuft (brush) cells exhibit the highest HVG blindness (21 percentile points). (b) Spatial distribution of Tuft cells at 8 µ m resolution reveals focal niches (red spots) with proportions up to 61%. (c) Stem cell distribution at 8 µ m shows concentration at crypt bases. (d) Resolution sensitivity of Tuft cell detection. Maximum proportion decreases from 61% (8 µ m) to 4% (128 µ m), rendering focal niches undetectable at conventional resolution. (e) Colocalization analysis reveals Tuft cell hotspots are enriched 16.8-fold for stem cells and 15.3-fold for enteroendocrine cells ( p < 10 −4 , permutation test), but depleted for differentiated cell types (enterocytes 0.11×, goblet cells 0.10×). (f) Spatial zoom showing Tuft-Stem co-localization at crypt bases (blue: Stem-high, pink: co-localized). Tuft cells rarely appear without adjacent stem cells, consistent with their intimate niche association. Data: Visium HD Mouse Small Intestine (10x Genomics).

Techniques: Marker, Selection, Concentration Assay

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1) Product Images from "FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching"

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