transcriptome st data Search Results


st transcriptome data  (RStudio)
90
RStudio st transcriptome data
Isolation and cluster analysis of single-cell <t>transcriptomes</t> from four glioma samples derived from two patients. (A) Workflow of experimental strategy: isolation of human glioma tissues during clinical surgery, followed by parallel scRNA-seq and Spatial Transcriptomic (ST) analysis profiling using with the 10x Genomics Chromium platform and subsequent validation by H&E and IHC staining. (B) Uniform Manifold Approximation and Projection (UMAP) plot showing 16 major clusters. (C) Clusters are annotated for their cell types as predicted using canonical markers and signature-based annotation using Garnett. (D) Heatmap showing clustering with top 30 highly expressed genes. (E, F) Feature UMAP plots depicting cluster-specific expression of cell clusters markers including SOX2 (SRY-Box Transcription Factor 2) and EGFR (Epidermal growth factor receptor) to indicate the major malignant cell clusters.
St Transcriptome Data, supplied by RStudio, 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/transcriptome+st+data/pmc11385306-140-8-16?v=RStudio
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st transcriptome data - by Bioz Stars, 2026-07
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mouse olfactory bulb st data  (Spatial Transcriptomics Inc)
86
Spatial Transcriptomics Inc mouse olfactory bulb st data
Analysis of <t>mouse</t> <t>olfactory</t> <t>bulb</t> <t>data.</t> a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).
Mouse Olfactory Bulb St Data, supplied by Spatial Transcriptomics Inc, 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/product/transcriptome+st+data/pmc12103236-331-3-9?v=Spatial+Transcriptomics+Inc
Average 86 stars, based on 1 article reviews
mouse olfactory bulb st data - by Bioz Stars, 2026-07
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st data include gene expression counts  (Spatial Transcriptomics Inc)
86
Spatial Transcriptomics Inc st data include gene expression counts
Analysis of <t>mouse</t> <t>olfactory</t> <t>bulb</t> <t>data.</t> a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).
St Data Include Gene Expression Counts, supplied by Spatial Transcriptomics Inc, 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/product/transcriptome+st+data/arxiv__2401__07543-215-2-0?v=Spatial+Transcriptomics+Inc
Average 86 stars, based on 1 article reviews
st data include gene expression counts - by Bioz Stars, 2026-07
86/100 stars
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st data demands models  (Spatial Transcriptomics Inc)
86
Spatial Transcriptomics Inc st data demands models
Analysis of <t>mouse</t> <t>olfactory</t> <t>bulb</t> <t>data.</t> a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).
St Data Demands Models, supplied by Spatial Transcriptomics Inc, 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/product/transcriptome+st+data/pmc12440005-0-2-0?v=Spatial+Transcriptomics+Inc
Average 86 stars, based on 1 article reviews
st data demands models - by Bioz Stars, 2026-07
86/100 stars
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analysis ○ xenium st data preparation ○ visium st data  (Spatial Transcriptomics Inc)
86
Spatial Transcriptomics Inc analysis ○ xenium st data preparation ○ visium st data
Analysis of <t>mouse</t> <t>olfactory</t> <t>bulb</t> <t>data.</t> a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).
Analysis ○ Xenium St Data Preparation ○ Visium St Data, supplied by Spatial Transcriptomics Inc, 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/product/transcriptome+st+data/pmc12208317__mmc8-240-108-105?v=Spatial+Transcriptomics+Inc
Average 86 stars, based on 1 article reviews
analysis ○ xenium st data preparation ○ visium st data - by Bioz Stars, 2026-07
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Image Search Results


Isolation and cluster analysis of single-cell transcriptomes from four glioma samples derived from two patients. (A) Workflow of experimental strategy: isolation of human glioma tissues during clinical surgery, followed by parallel scRNA-seq and Spatial Transcriptomic (ST) analysis profiling using with the 10x Genomics Chromium platform and subsequent validation by H&E and IHC staining. (B) Uniform Manifold Approximation and Projection (UMAP) plot showing 16 major clusters. (C) Clusters are annotated for their cell types as predicted using canonical markers and signature-based annotation using Garnett. (D) Heatmap showing clustering with top 30 highly expressed genes. (E, F) Feature UMAP plots depicting cluster-specific expression of cell clusters markers including SOX2 (SRY-Box Transcription Factor 2) and EGFR (Epidermal growth factor receptor) to indicate the major malignant cell clusters.

Journal: Frontiers in Immunology

Article Title: Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations

doi: 10.3389/fimmu.2024.1452172

Figure Lengend Snippet: Isolation and cluster analysis of single-cell transcriptomes from four glioma samples derived from two patients. (A) Workflow of experimental strategy: isolation of human glioma tissues during clinical surgery, followed by parallel scRNA-seq and Spatial Transcriptomic (ST) analysis profiling using with the 10x Genomics Chromium platform and subsequent validation by H&E and IHC staining. (B) Uniform Manifold Approximation and Projection (UMAP) plot showing 16 major clusters. (C) Clusters are annotated for their cell types as predicted using canonical markers and signature-based annotation using Garnett. (D) Heatmap showing clustering with top 30 highly expressed genes. (E, F) Feature UMAP plots depicting cluster-specific expression of cell clusters markers including SOX2 (SRY-Box Transcription Factor 2) and EGFR (Epidermal growth factor receptor) to indicate the major malignant cell clusters.

Article Snippet: For both HG1 and HG2, we used the ST transcriptome data and an enrichment study in R studio using the SPATA package (version 1.0.1).

Techniques: Isolation, Derivative Assay, Biomarker Discovery, Immunohistochemistry, Expressing

Cluster analysis and annotations of spatial transcriptomes from four glioma samples derived from two patients. (A) t-distributed stochastic neighbor embedding (tSNE) projection of spots showing 9 major ST clusters. (B) The spatial relationship among 9 major ST clusters in four different samples. (C) Heatmap showing clustering with top 30 highly expressed genes from each ST clusters. (D) Heatmap showing the correlation matrix generated from 8 major ST Clusters and 7 major cell types from scRNA-seq calculated through MIA (Multimodal intersection analysis).

Journal: Frontiers in Immunology

Article Title: Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations

doi: 10.3389/fimmu.2024.1452172

Figure Lengend Snippet: Cluster analysis and annotations of spatial transcriptomes from four glioma samples derived from two patients. (A) t-distributed stochastic neighbor embedding (tSNE) projection of spots showing 9 major ST clusters. (B) The spatial relationship among 9 major ST clusters in four different samples. (C) Heatmap showing clustering with top 30 highly expressed genes from each ST clusters. (D) Heatmap showing the correlation matrix generated from 8 major ST Clusters and 7 major cell types from scRNA-seq calculated through MIA (Multimodal intersection analysis).

Article Snippet: For both HG1 and HG2, we used the ST transcriptome data and an enrichment study in R studio using the SPATA package (version 1.0.1).

Techniques: Derivative Assay, Generated

Analysis of mouse olfactory bulb data. a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).

Journal: Small Methods

Article Title: Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics

doi: 10.1002/smtd.202401145

Figure Lengend Snippet: Analysis of mouse olfactory bulb data. a) H&E staining of the olfactory bulb (top) and the deconvolution results of all candidate methods displayed by the spatial scatter pie plot of cell‐type composition on each spatial location. The examined cell types were granule cells (GC), olfactory sensory neurons (OSNs), periglomerular cells (PGC), mitral/tufted cells (M‐TC), and external plexiform layer interneurons (EPL‐IN) b) Manual annotation of anatomic layers (top), including the granule cell layer (GCL), the mitral cell layer (MCL), the glomerular layer (GL), and the nerve layer (ONL), and the spatial domains of different deconvolution methods visualized by spatial scatters of specific domain types. c) Performance comparison between candidate deconvolution methods, including QR‐SIDE, STdeconvolve, CARDfree, RCTD, CARD, SPOTlight, and spatialDWLS in terms of NMI (left) and ARI (right). d) UMAP plots of gene expression for Topic 1, 2, 3 identified by QR‐SIDE. The color scheme of each topic domain was the same as in (b). e) The heatmap of normalized expression level for the top 10 DE genes for topic domain 1, 2, 3. f) The correlation between DE genes of identified domains and marker genes of each cell type. g) The mean expression level of an example marker gene list for QR‐SIDE, where Tyro3 was included as the interference marker gene of cell type GC. h) Left and middle panels: The estimated spot‐separable η scores of correct marker Penk and misclassified marker Tyro3 . Right panel: The line plots of mean η of all markers across all spatial spots and the RMSE between estimated cell‐type composition by varying the inclusion of top 3‐7 marker genes of each cell type as the input gene list and the deconvolution results using high‐quality marker genes (as shown in a).

Article Snippet: These include the mouse olfactory bulb ST data from Spatial Transcriptomics v1.0 ( https://www.spatialresearch.org ), the four human hepatocellular carcinoma Visium datasets ( https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=858545 ), mouse anterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Anterior ), and mouse posterior brain 10x Visium data ( https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Mouse_Brain_Sagittal_Posterior ).

Techniques: Staining, Comparison, Gene Expression, Expressing, Marker