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srt data of mouse kidney coronal section  (10X Genomics)

 
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    10X Genomics srt data of mouse kidney coronal section
    STged is specifically designed for deconvolving gene expression from low-resolution <t>SRT</t> <t>data.</t> ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.
    Srt Data Of Mouse Kidney Coronal Section, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/srt+data+of+mouse+kidney+coronal+section/pmc11838043-404-0-14?v=10X+Genomics
    Average 90 stars, based on 1 article reviews
    srt data of mouse kidney coronal section - by Bioz Stars, 2026-08
    90/100 stars

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    1) Product Images from "Precise gene expression deconvolution in spatial transcriptomics with STged"

    Article Title: Precise gene expression deconvolution in spatial transcriptomics with STged

    Journal: Nucleic Acids Research

    doi: 10.1093/nar/gkaf087

    STged is specifically designed for deconvolving gene expression from low-resolution SRT data. ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.
    Figure Legend Snippet: STged is specifically designed for deconvolving gene expression from low-resolution SRT data. ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.

    Techniques Used: Gene Expression, Construct, Derivative Assay

    Analysis of simulation SRT data generated from seqFISH+ data. ( A ) Visualization of the spatial distribution of cell types in the seqFISH+ mouse cortex. Each square grid represents a simulated spot containing multiple cells, with each color indicating a different cell type. The six cell types are: eNeuron, iNeuron, astrocytes, Olig, microglia, and endo-mural cells. ( B ) UMAP (Uniform Manifold Approximation and Projection) plot for the scRNA-seq data from mouse cortex tissue. This dataset contains 8091 genes and 1691 cells annotated into six cell types. (C–D) Visualization of the spatial distribution of cell-type proportions. ( C ) It shows the true cell-type proportions derived from panel (A). ( D ) It shows the cell-type proportions inferred by the deconvolution method of EnDecon . Each pie chart represents a point in the SRT data showing the cell-type composition. (E–F) Evaluation of the performance of the gene expression deconvolution methods in two different scenarios, based on four evaluation metrics. ( E ) Scenario 1: methods evaluated with true cell-type proportions from the simulation. ( F ) Scenario 2: methods evaluated using cell-type proportions estimated by original or recommended deconvolution methods. Each method is represented by a different color.
    Figure Legend Snippet: Analysis of simulation SRT data generated from seqFISH+ data. ( A ) Visualization of the spatial distribution of cell types in the seqFISH+ mouse cortex. Each square grid represents a simulated spot containing multiple cells, with each color indicating a different cell type. The six cell types are: eNeuron, iNeuron, astrocytes, Olig, microglia, and endo-mural cells. ( B ) UMAP (Uniform Manifold Approximation and Projection) plot for the scRNA-seq data from mouse cortex tissue. This dataset contains 8091 genes and 1691 cells annotated into six cell types. (C–D) Visualization of the spatial distribution of cell-type proportions. ( C ) It shows the true cell-type proportions derived from panel (A). ( D ) It shows the cell-type proportions inferred by the deconvolution method of EnDecon . Each pie chart represents a point in the SRT data showing the cell-type composition. (E–F) Evaluation of the performance of the gene expression deconvolution methods in two different scenarios, based on four evaluation metrics. ( E ) Scenario 1: methods evaluated with true cell-type proportions from the simulation. ( F ) Scenario 2: methods evaluated using cell-type proportions estimated by original or recommended deconvolution methods. Each method is represented by a different color.

    Techniques Used: Generated, Derivative Assay, Gene Expression

    Analysis of PDAC SRT data. ( A ) Histologist annotation of PDAC tissue sections divided into four distinct regions (right panel) based on H & E staining images (left panel) from the original study . ( B ) Spatial scatter pie chart representation of cell-type composition, where each pie represents a spot in the SRT data with cell-type proportion inferred by EnDecon . ( C ) Spatial distribution of fibroblast subpopulations, with different colors indicating distinct subgroups and gray representing spots without fibroblasts. ( D ) Bubble plot showing significant L-R pairs between malignant cells and macrophages, as inferred by CellChat . ( E ) Venn diagrams illustrating the overlap of significantly detected L-R pairs across four different datasets. ( F ) Distribution of PECAM1 expression across various cell types in different datasets. ( G ) The spatial distribution of ctHVGs in fibroblasts with the highest PCC scores for each cell type is shown, with the top panel for cancer clone A-specific ctHVGs and the bottom panel for endothelial-specific ctHVGs. ( H ) Schematic of biological insights into the communication underlying fibroblast-cancer clone A/endothelial cell interactions.
    Figure Legend Snippet: Analysis of PDAC SRT data. ( A ) Histologist annotation of PDAC tissue sections divided into four distinct regions (right panel) based on H & E staining images (left panel) from the original study . ( B ) Spatial scatter pie chart representation of cell-type composition, where each pie represents a spot in the SRT data with cell-type proportion inferred by EnDecon . ( C ) Spatial distribution of fibroblast subpopulations, with different colors indicating distinct subgroups and gray representing spots without fibroblasts. ( D ) Bubble plot showing significant L-R pairs between malignant cells and macrophages, as inferred by CellChat . ( E ) Venn diagrams illustrating the overlap of significantly detected L-R pairs across four different datasets. ( F ) Distribution of PECAM1 expression across various cell types in different datasets. ( G ) The spatial distribution of ctHVGs in fibroblasts with the highest PCC scores for each cell type is shown, with the top panel for cancer clone A-specific ctHVGs and the bottom panel for endothelial-specific ctHVGs. ( H ) Schematic of biological insights into the communication underlying fibroblast-cancer clone A/endothelial cell interactions.

    Techniques Used: Staining, Expressing

    Analysis of the SCC SRT data. ( A ) Representation of cell-type composition using a spatial scatter pie chart, where each pie represents a spot in the SRT data, with cell-type proportion estimated by EnDecon . ( B ) Visualization of the spatial distribution of dominant cell types based on tumor and nontumor classifications. Tumor cell proportions are calculated by summing the proportions of four tumor cell types (e.g. TSK, KC-Basal, KC-Cyc, and KC-Diff), while nontumor cell proportions are determined by summing the proportions of four to seven nontumor cell types. ( C ) Visualization of the spatial distribution of TSK subpopulations, with different colors indicating distinct subgroups and gray representing spots without TSKs. ( D ) Presentation of Venn diagrams to show the overlap of significantly detected L-R pairs across four different datasets. ( E ) Bubble plot displays inferred cell-type interaction pairs between different cell types using CellChat based on STged-deconvoluted and raw SRT data. ( F ) Examination of CDH1 expression distribution between TSK and fibroblast cells. ( G ) Schematic illustration of CDH1 gene self-regulation dynamics between fibroblast-dominant and TSK-dominant spots derived from raw SRT data. ( H ) Left panel shows cell-type distribution and right panel displays the spatial distribution of TSK-associated ctHVGs with the highest PCC scores. ( I ) Schematic illustration to elucidate the biological interactions and communication between TSK and macrophage cells and fibroblast cells.
    Figure Legend Snippet: Analysis of the SCC SRT data. ( A ) Representation of cell-type composition using a spatial scatter pie chart, where each pie represents a spot in the SRT data, with cell-type proportion estimated by EnDecon . ( B ) Visualization of the spatial distribution of dominant cell types based on tumor and nontumor classifications. Tumor cell proportions are calculated by summing the proportions of four tumor cell types (e.g. TSK, KC-Basal, KC-Cyc, and KC-Diff), while nontumor cell proportions are determined by summing the proportions of four to seven nontumor cell types. ( C ) Visualization of the spatial distribution of TSK subpopulations, with different colors indicating distinct subgroups and gray representing spots without TSKs. ( D ) Presentation of Venn diagrams to show the overlap of significantly detected L-R pairs across four different datasets. ( E ) Bubble plot displays inferred cell-type interaction pairs between different cell types using CellChat based on STged-deconvoluted and raw SRT data. ( F ) Examination of CDH1 expression distribution between TSK and fibroblast cells. ( G ) Schematic illustration of CDH1 gene self-regulation dynamics between fibroblast-dominant and TSK-dominant spots derived from raw SRT data. ( H ) Left panel shows cell-type distribution and right panel displays the spatial distribution of TSK-associated ctHVGs with the highest PCC scores. ( I ) Schematic illustration to elucidate the biological interactions and communication between TSK and macrophage cells and fibroblast cells.

    Techniques Used: Expressing, Derivative Assay



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    srt data of mouse kidney coronal section  (10X Genomics)
    90
    10X Genomics srt data of mouse kidney coronal section
    STged is specifically designed for deconvolving gene expression from low-resolution <t>SRT</t> <t>data.</t> ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.
    Srt Data Of Mouse Kidney Coronal Section, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/srt+data+of+mouse+kidney+coronal+section/pmc11838043-404-0-14?v=10X+Genomics
    Average 90 stars, based on 1 article reviews
    srt data of mouse kidney coronal section - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier

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    STged is specifically designed for deconvolving gene expression from low-resolution SRT data. ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.

    Journal: Nucleic Acids Research

    Article Title: Precise gene expression deconvolution in spatial transcriptomics with STged

    doi: 10.1093/nar/gkaf087

    Figure Lengend Snippet: STged is specifically designed for deconvolving gene expression from low-resolution SRT data. ( A ) Inputs for STged include an SRT gene expression matrix with coordinate information (top panel), corresponding cell-type proportion information (middle panel), and annotated scRNA-seq data as a reference for cell-type-specific gene expression (bottom panel). ( B ) STged’s computational model utilizes graph-based and reference gene signature-guided approaches, integrating cell-type-specific gene expression data from both spatial neighbor data and matched tissue scRNA-seq data. A spatial neighbor graph is constructed using spot location information, and cell-type-specific gene expression is derived from the annotated scRNA-seq data. ( C ) Outputs from STged include spot- and cell-type-specific gene expression matrices, alongside detailed gene expression profiles for each spot. ( D ) STged reconstructs spot- and cell-type-specific gene expression data for various downstream analyses, using cell- and gene-level approaches to thoroughly understand spatial cellular heterogeneity. At the cell level, clustering identifies distinct cell populations and continuous trajectories, and CCC analysis helps elucidate signaling interactions among cell types. At the gene level, mHVGs analysis identifies gene expression levels that vary across spatial microenvironments, and gene expression program analysis investigates the coordinated roles of gene sets in cellular regulation.

    Article Snippet: SRT data of mouse kidney coronal section generated are downloaded from the website: ( https://www.10xgenomics.com/cn/resources/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

    Techniques: Gene Expression, Construct, Derivative Assay

    Analysis of simulation SRT data generated from seqFISH+ data. ( A ) Visualization of the spatial distribution of cell types in the seqFISH+ mouse cortex. Each square grid represents a simulated spot containing multiple cells, with each color indicating a different cell type. The six cell types are: eNeuron, iNeuron, astrocytes, Olig, microglia, and endo-mural cells. ( B ) UMAP (Uniform Manifold Approximation and Projection) plot for the scRNA-seq data from mouse cortex tissue. This dataset contains 8091 genes and 1691 cells annotated into six cell types. (C–D) Visualization of the spatial distribution of cell-type proportions. ( C ) It shows the true cell-type proportions derived from panel (A). ( D ) It shows the cell-type proportions inferred by the deconvolution method of EnDecon . Each pie chart represents a point in the SRT data showing the cell-type composition. (E–F) Evaluation of the performance of the gene expression deconvolution methods in two different scenarios, based on four evaluation metrics. ( E ) Scenario 1: methods evaluated with true cell-type proportions from the simulation. ( F ) Scenario 2: methods evaluated using cell-type proportions estimated by original or recommended deconvolution methods. Each method is represented by a different color.

    Journal: Nucleic Acids Research

    Article Title: Precise gene expression deconvolution in spatial transcriptomics with STged

    doi: 10.1093/nar/gkaf087

    Figure Lengend Snippet: Analysis of simulation SRT data generated from seqFISH+ data. ( A ) Visualization of the spatial distribution of cell types in the seqFISH+ mouse cortex. Each square grid represents a simulated spot containing multiple cells, with each color indicating a different cell type. The six cell types are: eNeuron, iNeuron, astrocytes, Olig, microglia, and endo-mural cells. ( B ) UMAP (Uniform Manifold Approximation and Projection) plot for the scRNA-seq data from mouse cortex tissue. This dataset contains 8091 genes and 1691 cells annotated into six cell types. (C–D) Visualization of the spatial distribution of cell-type proportions. ( C ) It shows the true cell-type proportions derived from panel (A). ( D ) It shows the cell-type proportions inferred by the deconvolution method of EnDecon . Each pie chart represents a point in the SRT data showing the cell-type composition. (E–F) Evaluation of the performance of the gene expression deconvolution methods in two different scenarios, based on four evaluation metrics. ( E ) Scenario 1: methods evaluated with true cell-type proportions from the simulation. ( F ) Scenario 2: methods evaluated using cell-type proportions estimated by original or recommended deconvolution methods. Each method is represented by a different color.

    Article Snippet: SRT data of mouse kidney coronal section generated are downloaded from the website: ( https://www.10xgenomics.com/cn/resources/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

    Techniques: Generated, Derivative Assay, Gene Expression

    Analysis of PDAC SRT data. ( A ) Histologist annotation of PDAC tissue sections divided into four distinct regions (right panel) based on H & E staining images (left panel) from the original study . ( B ) Spatial scatter pie chart representation of cell-type composition, where each pie represents a spot in the SRT data with cell-type proportion inferred by EnDecon . ( C ) Spatial distribution of fibroblast subpopulations, with different colors indicating distinct subgroups and gray representing spots without fibroblasts. ( D ) Bubble plot showing significant L-R pairs between malignant cells and macrophages, as inferred by CellChat . ( E ) Venn diagrams illustrating the overlap of significantly detected L-R pairs across four different datasets. ( F ) Distribution of PECAM1 expression across various cell types in different datasets. ( G ) The spatial distribution of ctHVGs in fibroblasts with the highest PCC scores for each cell type is shown, with the top panel for cancer clone A-specific ctHVGs and the bottom panel for endothelial-specific ctHVGs. ( H ) Schematic of biological insights into the communication underlying fibroblast-cancer clone A/endothelial cell interactions.

    Journal: Nucleic Acids Research

    Article Title: Precise gene expression deconvolution in spatial transcriptomics with STged

    doi: 10.1093/nar/gkaf087

    Figure Lengend Snippet: Analysis of PDAC SRT data. ( A ) Histologist annotation of PDAC tissue sections divided into four distinct regions (right panel) based on H & E staining images (left panel) from the original study . ( B ) Spatial scatter pie chart representation of cell-type composition, where each pie represents a spot in the SRT data with cell-type proportion inferred by EnDecon . ( C ) Spatial distribution of fibroblast subpopulations, with different colors indicating distinct subgroups and gray representing spots without fibroblasts. ( D ) Bubble plot showing significant L-R pairs between malignant cells and macrophages, as inferred by CellChat . ( E ) Venn diagrams illustrating the overlap of significantly detected L-R pairs across four different datasets. ( F ) Distribution of PECAM1 expression across various cell types in different datasets. ( G ) The spatial distribution of ctHVGs in fibroblasts with the highest PCC scores for each cell type is shown, with the top panel for cancer clone A-specific ctHVGs and the bottom panel for endothelial-specific ctHVGs. ( H ) Schematic of biological insights into the communication underlying fibroblast-cancer clone A/endothelial cell interactions.

    Article Snippet: SRT data of mouse kidney coronal section generated are downloaded from the website: ( https://www.10xgenomics.com/cn/resources/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

    Techniques: Staining, Expressing

    Analysis of the SCC SRT data. ( A ) Representation of cell-type composition using a spatial scatter pie chart, where each pie represents a spot in the SRT data, with cell-type proportion estimated by EnDecon . ( B ) Visualization of the spatial distribution of dominant cell types based on tumor and nontumor classifications. Tumor cell proportions are calculated by summing the proportions of four tumor cell types (e.g. TSK, KC-Basal, KC-Cyc, and KC-Diff), while nontumor cell proportions are determined by summing the proportions of four to seven nontumor cell types. ( C ) Visualization of the spatial distribution of TSK subpopulations, with different colors indicating distinct subgroups and gray representing spots without TSKs. ( D ) Presentation of Venn diagrams to show the overlap of significantly detected L-R pairs across four different datasets. ( E ) Bubble plot displays inferred cell-type interaction pairs between different cell types using CellChat based on STged-deconvoluted and raw SRT data. ( F ) Examination of CDH1 expression distribution between TSK and fibroblast cells. ( G ) Schematic illustration of CDH1 gene self-regulation dynamics between fibroblast-dominant and TSK-dominant spots derived from raw SRT data. ( H ) Left panel shows cell-type distribution and right panel displays the spatial distribution of TSK-associated ctHVGs with the highest PCC scores. ( I ) Schematic illustration to elucidate the biological interactions and communication between TSK and macrophage cells and fibroblast cells.

    Journal: Nucleic Acids Research

    Article Title: Precise gene expression deconvolution in spatial transcriptomics with STged

    doi: 10.1093/nar/gkaf087

    Figure Lengend Snippet: Analysis of the SCC SRT data. ( A ) Representation of cell-type composition using a spatial scatter pie chart, where each pie represents a spot in the SRT data, with cell-type proportion estimated by EnDecon . ( B ) Visualization of the spatial distribution of dominant cell types based on tumor and nontumor classifications. Tumor cell proportions are calculated by summing the proportions of four tumor cell types (e.g. TSK, KC-Basal, KC-Cyc, and KC-Diff), while nontumor cell proportions are determined by summing the proportions of four to seven nontumor cell types. ( C ) Visualization of the spatial distribution of TSK subpopulations, with different colors indicating distinct subgroups and gray representing spots without TSKs. ( D ) Presentation of Venn diagrams to show the overlap of significantly detected L-R pairs across four different datasets. ( E ) Bubble plot displays inferred cell-type interaction pairs between different cell types using CellChat based on STged-deconvoluted and raw SRT data. ( F ) Examination of CDH1 expression distribution between TSK and fibroblast cells. ( G ) Schematic illustration of CDH1 gene self-regulation dynamics between fibroblast-dominant and TSK-dominant spots derived from raw SRT data. ( H ) Left panel shows cell-type distribution and right panel displays the spatial distribution of TSK-associated ctHVGs with the highest PCC scores. ( I ) Schematic illustration to elucidate the biological interactions and communication between TSK and macrophage cells and fibroblast cells.

    Article Snippet: SRT data of mouse kidney coronal section generated are downloaded from the website: ( https://www.10xgenomics.com/cn/resources/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

    Techniques: Expressing, Derivative Assay