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Spatial Transcriptomics Inc spatial transcriptomics (st)
Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial <t>transcriptomics</t> simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
Spatial Transcriptomics (St), 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
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Average 90 stars, based on 1 article reviews
spatial transcriptomics (st) - by Bioz Stars, 2026-04
90/100 stars

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1) Product Images from "STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition"

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition

Journal: Genome Biology

doi: 10.1186/s13059-025-03608-4

Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial transcriptomics simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
Figure Legend Snippet: Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial transcriptomics simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve

Techniques Used: Gene Expression, Histopathology, Comparison, Expressing, Marker



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Spatial Transcriptomics Inc spatial transcriptomics (st)
Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial <t>transcriptomics</t> simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
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https://www.bioz.com/result/spatial transcriptomics (st)/product/Spatial Transcriptomics Inc
Average 90 stars, based on 1 article reviews
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Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial <t>transcriptomics</t> simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
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Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial <t>transcriptomics</t> simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
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Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial <t>transcriptomics</t> simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve
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Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial transcriptomics simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve

Journal: Genome Biology

Article Title: STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition

doi: 10.1186/s13059-025-03608-4

Figure Lengend Snippet: Spot-level predictions and benchmarking of a zoomed patch in a colon cancer VisiumHD sample. a Example of STHD results of a patch from the human colon cancer VisiumHD sample, revealing tumor-like epithelial cells and intercryptic immune population, visualized using the interactive STHDviewer. b Cell type posterior probabilities at spot level for the same patch, visualized using Squidpy. Top row, three epithelial cell types; Bottom row, two immune cell types. c A spatial plot of unsupervised Leiden clustering on spot gene expression. d Spatial plots of unsupervised Leiden clustering of gene expression aggregated by bins in size of 4 × 4 spots. Left, clustering bins within patch, right, global cluster group of bins. e Cell type proportion decomposed by RCTD on bins of size of 4 × 4 spots, demonstrating two tumor epithelial classes, enterocytes, and stem-like cells. f The H&E histopathology image in full resolution of the same crop. g Comparison to other cell typing and clustering methods adaptive to high-resolution spots. h The STHD predicted labels at spot-level and STHD-guided binning in bins of 4 × 4 spots and 8 × 8 spots, visualized using Squidpy using the same colon cancer cell type colormap. i Benchmarking with simulated high-resolution spatial data. Left, ground truth cell type labels; middle, STHD predicted cell type labels; right, receiver operating characteristic curve (ROC) curve for all cell types. j Benchmarking against other methods for spot annotation in repeated spatial transcriptomics simulations. k Expression dot plots for marker genes of normal epithelial cells, at spot level or STHD-guided bin level. Left to right: normal epithelial cell types in reference human colon cancer sample, normal epithelial cell types based on STHD predicted spots, normal epithelial cell types from STHD-guided bins of size 4 × 4 spots, normal epithelial cell types in STHD-guided bins of size 8 × 8 spots. The same cell type-specific gene markers from the original colon cancer study were used. ROC, Receiver operating characteristic. AUC, area under the curve

Article Snippet: Spatial transcriptomics (ST) technologies have enabled gene expression profiling in the native spatial context of tissues.

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