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mouse olfactory bulb tissue  (Spatial Transcriptomics Inc)

 
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    Structured Review

    Spatial Transcriptomics Inc mouse olfactory bulb tissue
    a, Volcano plots for highly associated genes with PC1 image latent features from <t>olfactory</t> and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory <t>bulb</t> and 1.2 in kidney <t>tissue.</t> Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.
    Mouse Olfactory Bulb Tissue, supplied by Spatial Transcriptomics Inc, 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/result/mouse olfactory bulb tissue/product/Spatial Transcriptomics Inc
    Average 90 stars, based on 1 article reviews
    mouse olfactory bulb tissue - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image"

    Article Title: Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image

    Journal: bioRxiv

    doi: 10.1101/2020.06.15.150698

    a, Volcano plots for highly associated genes with PC1 image latent features from olfactory and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory bulb and 1.2 in kidney tissue. Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.
    Figure Legend Snippet: a, Volcano plots for highly associated genes with PC1 image latent features from olfactory and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory bulb and 1.2 in kidney tissue. Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.

    Techniques Used:

    a, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space in olfactory bulb tissue. b, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from olfactory bulb tissue. c, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space from kidney tissue. d, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from kidney tissue. e, Gene ontology (GO) analysis for SPADE genes from PC1 image latent in olfactory bulb data. Top 10 GO terms for each subcategory, molecular function (MF), cellular component (CC), and biological process (BP) were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap f, GO analysis for SPADE genes from PC2 image latent in kidney data. Top 10 GO terms for each subcategory, MF, CC, and BP were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap
    Figure Legend Snippet: a, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space in olfactory bulb tissue. b, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from olfactory bulb tissue. c, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space from kidney tissue. d, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from kidney tissue. e, Gene ontology (GO) analysis for SPADE genes from PC1 image latent in olfactory bulb data. Top 10 GO terms for each subcategory, molecular function (MF), cellular component (CC), and biological process (BP) were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap f, GO analysis for SPADE genes from PC2 image latent in kidney data. Top 10 GO terms for each subcategory, MF, CC, and BP were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap

    Techniques Used: Expressing

    a, t-SNE plot of transcriptomic data and deep learning-derived image latent features in olfactory bulb tissue. SPADE genes were utilized to classify spots into 5 SPADE-based clusters and the cluster identity of each spot was visualized. b, t-SNE plot of transcriptomic data and deep learning-derived image latent features in kidney tissue. SPADE genes were utilized to classify spots into 10 SPADE-based clusters and the cluster identity of each spot was visualized. c, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in olfactory bulb tissue. d, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in kidney tissue. e, Spatial distribution of SPADE and HVG-based spot clusters mapped on the olfactory bulb tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. f, Spatial distribution of SPADE and HVG-based spot clusters mapped on the kidney tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. g, Spatial mapping of the SPADE 1-HVG 1 and SPADE 4-HVG 4 matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. M is abbreviation for matched cluster and MM is for mismatched cluster. h, PC1 and PC2 image latent plot for matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. A distribution of the mismatched spot clusters was presented in green dots. 95% confidence ellipse for each cluster based on multivariate t-distribution was exhibited on PC plot. Enlarged picture on the top shows median distance between mismatched spots and center of mass for matched clusters.
    Figure Legend Snippet: a, t-SNE plot of transcriptomic data and deep learning-derived image latent features in olfactory bulb tissue. SPADE genes were utilized to classify spots into 5 SPADE-based clusters and the cluster identity of each spot was visualized. b, t-SNE plot of transcriptomic data and deep learning-derived image latent features in kidney tissue. SPADE genes were utilized to classify spots into 10 SPADE-based clusters and the cluster identity of each spot was visualized. c, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in olfactory bulb tissue. d, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in kidney tissue. e, Spatial distribution of SPADE and HVG-based spot clusters mapped on the olfactory bulb tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. f, Spatial distribution of SPADE and HVG-based spot clusters mapped on the kidney tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. g, Spatial mapping of the SPADE 1-HVG 1 and SPADE 4-HVG 4 matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. M is abbreviation for matched cluster and MM is for mismatched cluster. h, PC1 and PC2 image latent plot for matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. A distribution of the mismatched spot clusters was presented in green dots. 95% confidence ellipse for each cluster based on multivariate t-distribution was exhibited on PC plot. Enlarged picture on the top shows median distance between mismatched spots and center of mass for matched clusters.

    Techniques Used: Derivative Assay



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    mouse olfactory bulb tissue  (Spatial Transcriptomics Inc)
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    Spatial Transcriptomics Inc mouse olfactory bulb tissue
    a, Volcano plots for highly associated genes with PC1 image latent features from <t>olfactory</t> and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory <t>bulb</t> and 1.2 in kidney <t>tissue.</t> Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.
    Mouse Olfactory Bulb Tissue, supplied by Spatial Transcriptomics Inc, 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/result/mouse olfactory bulb tissue/product/Spatial Transcriptomics Inc
    Average 90 stars, based on 1 article reviews
    mouse olfactory bulb tissue - by Bioz Stars, 2026-04
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    Image Search Results


    a, Volcano plots for highly associated genes with PC1 image latent features from olfactory and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory bulb and 1.2 in kidney tissue. Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.

    Journal: bioRxiv

    Article Title: Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image

    doi: 10.1101/2020.06.15.150698

    Figure Lengend Snippet: a, Volcano plots for highly associated genes with PC1 image latent features from olfactory and kidney tissues. Cutoff for log 2 regression coefficient (RC) is 0.045 in olfactory bulb and 1.2 in kidney tissue. Cutoff for adjusted p-value (Benjamini-Hochberg correction) is 10 −10 for both tissues. b, Volcano plots for highly associated genes with PC2 image latent features from olfactory bulb and kidney data. Cutoff for log 2 RC is 0.02 and 0.7 and cutoff for adjusted p-value (Benjamini-Hochberg correction) is 0.05 and 10 −10 , respectively. c, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from olfactory bulb tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was shown on top d, Heatmap for top 30 highly associated genes for log 2 RC in PC1 image latent space from kidney tissue. Hierarchical clustering was performed for top 30 genes and PC1 value in each of the spot was presented on top.

    Article Snippet: Utility of SPADE was further validated with mouse olfactory bulb tissue analyzed by a spatial transcriptomics paper and mouse kidney tissue from 10x genomics.

    Techniques:

    a, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space in olfactory bulb tissue. b, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from olfactory bulb tissue. c, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space from kidney tissue. d, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from kidney tissue. e, Gene ontology (GO) analysis for SPADE genes from PC1 image latent in olfactory bulb data. Top 10 GO terms for each subcategory, molecular function (MF), cellular component (CC), and biological process (BP) were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap f, GO analysis for SPADE genes from PC2 image latent in kidney data. Top 10 GO terms for each subcategory, MF, CC, and BP were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap

    Journal: bioRxiv

    Article Title: Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image

    doi: 10.1101/2020.06.15.150698

    Figure Lengend Snippet: a, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space in olfactory bulb tissue. b, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from olfactory bulb tissue. c, Spatial expression of top 5 genes representing greatest contrast in PC1 image latent space from kidney tissue. d, Spatial expression of top 5 genes representing greatest contrast in PC2 image latent space from kidney tissue. e, Gene ontology (GO) analysis for SPADE genes from PC1 image latent in olfactory bulb data. Top 10 GO terms for each subcategory, molecular function (MF), cellular component (CC), and biological process (BP) were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap f, GO analysis for SPADE genes from PC2 image latent in kidney data. Top 10 GO terms for each subcategory, MF, CC, and BP were exhibited. Number of overlapped genes was expressed as size of dot and Benjamini-Hochberg adjusted p-value was exhibited with colormap

    Article Snippet: Utility of SPADE was further validated with mouse olfactory bulb tissue analyzed by a spatial transcriptomics paper and mouse kidney tissue from 10x genomics.

    Techniques: Expressing

    a, t-SNE plot of transcriptomic data and deep learning-derived image latent features in olfactory bulb tissue. SPADE genes were utilized to classify spots into 5 SPADE-based clusters and the cluster identity of each spot was visualized. b, t-SNE plot of transcriptomic data and deep learning-derived image latent features in kidney tissue. SPADE genes were utilized to classify spots into 10 SPADE-based clusters and the cluster identity of each spot was visualized. c, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in olfactory bulb tissue. d, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in kidney tissue. e, Spatial distribution of SPADE and HVG-based spot clusters mapped on the olfactory bulb tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. f, Spatial distribution of SPADE and HVG-based spot clusters mapped on the kidney tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. g, Spatial mapping of the SPADE 1-HVG 1 and SPADE 4-HVG 4 matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. M is abbreviation for matched cluster and MM is for mismatched cluster. h, PC1 and PC2 image latent plot for matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. A distribution of the mismatched spot clusters was presented in green dots. 95% confidence ellipse for each cluster based on multivariate t-distribution was exhibited on PC plot. Enlarged picture on the top shows median distance between mismatched spots and center of mass for matched clusters.

    Journal: bioRxiv

    Article Title: Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image

    doi: 10.1101/2020.06.15.150698

    Figure Lengend Snippet: a, t-SNE plot of transcriptomic data and deep learning-derived image latent features in olfactory bulb tissue. SPADE genes were utilized to classify spots into 5 SPADE-based clusters and the cluster identity of each spot was visualized. b, t-SNE plot of transcriptomic data and deep learning-derived image latent features in kidney tissue. SPADE genes were utilized to classify spots into 10 SPADE-based clusters and the cluster identity of each spot was visualized. c, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in olfactory bulb tissue. d, A cross table exhibiting number of spots overlapped in corresponding SPADE and HVG-based clusters in kidney tissue. e, Spatial distribution of SPADE and HVG-based spot clusters mapped on the olfactory bulb tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. f, Spatial distribution of SPADE and HVG-based spot clusters mapped on the kidney tissue. The cluster numbers for SPADE or HVG-based cluster were exhibited on the right panel. g, Spatial mapping of the SPADE 1-HVG 1 and SPADE 4-HVG 4 matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. M is abbreviation for matched cluster and MM is for mismatched cluster. h, PC1 and PC2 image latent plot for matched and SPADE 4-HVG 1 mismatched spot clusters in olfactory bulb tissue. A distribution of the mismatched spot clusters was presented in green dots. 95% confidence ellipse for each cluster based on multivariate t-distribution was exhibited on PC plot. Enlarged picture on the top shows median distance between mismatched spots and center of mass for matched clusters.

    Article Snippet: Utility of SPADE was further validated with mouse olfactory bulb tissue analyzed by a spatial transcriptomics paper and mouse kidney tissue from 10x genomics.

    Techniques: Derivative Assay