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cngb stomics database  (Complete Genomics Inc)


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    Structured Review

    Complete Genomics Inc cngb stomics database
    Cngb Stomics Database, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 451 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stomics+database/Stereo-seq+Transcriptomics+Set+for+FFPE/bio_rxiv__64898__2026__04__08__717315-372-10-11
    Average 99 stars, based on 451 article reviews
    cngb stomics database - by Bioz Stars, 2026-09
    99/100 stars

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    Related Articles

    Expressing:

    Article Title: Advancing Cardiovascular Research With Single-Cell and Spatial Transcriptomics
    Article Snippet: Single-cell and spatial transcriptomics technologies have transformed the landscape of cardiovascular research, leading to novel insights into cellular heterogeneity and tissue architecture in health and disease.. These technologies enable researchers to deconvolute complex tissues and map gene expression patterns within their spatial contexts, providing critical information on the interplay between cell types and pathways affecting tissue regeneration or progression to fibrosis.. This review presents an overview of the recently developed applications of single-cell and spatial transcriptomics methods and their impact on cardiovascular research.

    Article Title: A Cori cycle-related gene signature predicts prognosis, immune microenvironment, and drug response in breast cancer
    Article Snippet: .. Single-cell expression levels of CCRGs were obtained from the stomics database ( https://db.cngb.org/stomics/analysis/ ) using datasets STDS0000027, STDS0000038, and STDS0000049. ..

    Article Title: Comprehensive Breslow thickness (BT)-based analysis to identify biological mechanisms associated with melanoma pathogenesis.
    Article Snippet: Breslow thickness (BT), a parameter measuring the depth of invasion of abnormally proliferating melanocytes, is a key indicator of melanoma severity and prognosis.. However, the mechanisms underlying the increase in BT remain elusive.. Utilizing data from The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma (SKCM), we identified a set of BT-related molecules and analyzed their expression and genomic heterogeneity across pancancerous and normal tissues.

    Marker:

    Article Title: Advancing Cardiovascular Research With Single-Cell and Spatial Transcriptomics
    Article Snippet: Single-cell and spatial transcriptomics technologies have transformed the landscape of cardiovascular research, leading to novel insights into cellular heterogeneity and tissue architecture in health and disease.. These technologies enable researchers to deconvolute complex tissues and map gene expression patterns within their spatial contexts, providing critical information on the interplay between cell types and pathways affecting tissue regeneration or progression to fibrosis.. This review presents an overview of the recently developed applications of single-cell and spatial transcriptomics methods and their impact on cardiovascular research.

    Gene Expression:

    Article Title: Advancing Cardiovascular Research With Single-Cell and Spatial Transcriptomics
    Article Snippet: Single-cell and spatial transcriptomics technologies have transformed the landscape of cardiovascular research, leading to novel insights into cellular heterogeneity and tissue architecture in health and disease.. These technologies enable researchers to deconvolute complex tissues and map gene expression patterns within their spatial contexts, providing critical information on the interplay between cell types and pathways affecting tissue regeneration or progression to fibrosis.. This review presents an overview of the recently developed applications of single-cell and spatial transcriptomics methods and their impact on cardiovascular research.

    Comparison:

    Article Title: Advancing Cardiovascular Research With Single-Cell and Spatial Transcriptomics
    Article Snippet: Single-cell and spatial transcriptomics technologies have transformed the landscape of cardiovascular research, leading to novel insights into cellular heterogeneity and tissue architecture in health and disease.. These technologies enable researchers to deconvolute complex tissues and map gene expression patterns within their spatial contexts, providing critical information on the interplay between cell types and pathways affecting tissue regeneration or progression to fibrosis.. This review presents an overview of the recently developed applications of single-cell and spatial transcriptomics methods and their impact on cardiovascular research.

    Olfactory:

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: 438 Availability of Source Code and Requirements 439 Project name: EAGS: efficient and adaptive Gaussian smoothing 440 Project home page: https://github.com/STOmics/EAGS 441 Operating system(s): Platform independent 442 Programming language: Python 443 24 Other requirements: Python 3.8 or higher 444 License: MIT License 445 RRID: SCR_024399 446 BiotoolsID: EAGS 447 Data availability 448 The mouse brain dataset at single-cell resolution is available in STOmics DB of China National 449 Gene Bank (CNGB) (accession code: “STT0000022”) [43], [44]. .. The mouse olfactory bulb at 450 single-cell resolution is available in STOMICS DataBase (accession code: “STT0000027”) [43], 451 [44]. ..

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: .. 472 Availability of Source Code and Requirements 473 Project name: EAGS: efficient and adaptive Gaussian smoothing 474 Project home page: https://github.com/STOmics/EAGS 475 Operating system(s): Platform independent 476 Programming language: Python 477 Other requirements: Python 3.8 or higher 478 License: MIT License 479 RRID: SCR_024399 480 BiotoolsID: EAGS 481 Data availability 482 Mouse brain at single-cell resolution and mouse olfactory bulb data at single-cell resolution have 483 been published in other studies, where the mouse brain data is available in China National Gene 484 25 Bank (CNGB) (https://db.cngb.org/cnsa/, accession code: “CNP0002966”), and the mouse olfactory 485 bulb is available in STOMICS DataBase (https://db.cngb.org/stomics/, accession code: 486 “STT0000027”). ..

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: .. The mouse olfactory bulb at single-cell resolution is available in the STOMICS DataBase (accession code: “STT0000027”) [ , ]. ..

    Single Cell:

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: 438 Availability of Source Code and Requirements 439 Project name: EAGS: efficient and adaptive Gaussian smoothing 440 Project home page: https://github.com/STOmics/EAGS 441 Operating system(s): Platform independent 442 Programming language: Python 443 24 Other requirements: Python 3.8 or higher 444 License: MIT License 445 RRID: SCR_024399 446 BiotoolsID: EAGS 447 Data availability 448 The mouse brain dataset at single-cell resolution is available in STOmics DB of China National 449 Gene Bank (CNGB) (accession code: “STT0000022”) [43], [44]. .. The mouse olfactory bulb at 450 single-cell resolution is available in STOMICS DataBase (accession code: “STT0000027”) [43], 451 [44]. ..

    Article Title: A Cori cycle-related gene signature predicts prognosis, immune microenvironment, and drug response in breast cancer
    Article Snippet: .. Single-cell expression levels of CCRGs were obtained from the stomics database ( https://db.cngb.org/stomics/analysis/ ) using datasets STDS0000027, STDS0000038, and STDS0000049. ..

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: .. 472 Availability of Source Code and Requirements 473 Project name: EAGS: efficient and adaptive Gaussian smoothing 474 Project home page: https://github.com/STOmics/EAGS 475 Operating system(s): Platform independent 476 Programming language: Python 477 Other requirements: Python 3.8 or higher 478 License: MIT License 479 RRID: SCR_024399 480 BiotoolsID: EAGS 481 Data availability 482 Mouse brain at single-cell resolution and mouse olfactory bulb data at single-cell resolution have 483 been published in other studies, where the mouse brain data is available in China National Gene 484 25 Bank (CNGB) (https://db.cngb.org/cnsa/, accession code: “CNP0002966”), and the mouse olfactory 485 bulb is available in STOMICS DataBase (https://db.cngb.org/stomics/, accession code: 486 “STT0000027”). ..

    Article Title: EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics
    Article Snippet: .. The mouse olfactory bulb at single-cell resolution is available in the STOMICS DataBase (accession code: “STT0000027”) [ , ]. ..



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    Complete Genomics Inc stomics db transcription database
    Single-cell analysis of SUSD3 in various cancers. (A) SUSD3 expression summary of 32 cell types from 77 single-cell datasets. (B) The scatter plot shows the distribution of 10 different cell types in the GSE103322HINSC dataset, as well as SUSD3 expression levels of cells in the GSE103322 dataset. (C) Spatial <t>transcription</t> slices showed the spatial expression of SUSD3, CD68, and CD163 markers, and the color of the dots indicated the expression level of the markers. (D) The scatter plot shows the distribution of 10 different cell types in the GSE120575 dataset, as well as SUSD3 expression levels of cells in the GSE120575 dataset. (E) Spatial transcription slices showed the spatial expression of SUSD3, ANXA1, and CCR7 markers, and the color of the dots indicated the expression level of the markers.
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    Image Search Results


    Single-cell analysis of SUSD3 in various cancers. (A) SUSD3 expression summary of 32 cell types from 77 single-cell datasets. (B) The scatter plot shows the distribution of 10 different cell types in the GSE103322HINSC dataset, as well as SUSD3 expression levels of cells in the GSE103322 dataset. (C) Spatial transcription slices showed the spatial expression of SUSD3, CD68, and CD163 markers, and the color of the dots indicated the expression level of the markers. (D) The scatter plot shows the distribution of 10 different cell types in the GSE120575 dataset, as well as SUSD3 expression levels of cells in the GSE120575 dataset. (E) Spatial transcription slices showed the spatial expression of SUSD3, ANXA1, and CCR7 markers, and the color of the dots indicated the expression level of the markers.

    Journal: Frontiers in Immunology

    Article Title: Exploration of SUSD3 in pan-cancer: studying its role, predictive analysis, and biological significance in various malignant tumors in humans

    doi: 10.3389/fimmu.2025.1521965

    Figure Lengend Snippet: Single-cell analysis of SUSD3 in various cancers. (A) SUSD3 expression summary of 32 cell types from 77 single-cell datasets. (B) The scatter plot shows the distribution of 10 different cell types in the GSE103322HINSC dataset, as well as SUSD3 expression levels of cells in the GSE103322 dataset. (C) Spatial transcription slices showed the spatial expression of SUSD3, CD68, and CD163 markers, and the color of the dots indicated the expression level of the markers. (D) The scatter plot shows the distribution of 10 different cell types in the GSE120575 dataset, as well as SUSD3 expression levels of cells in the GSE120575 dataset. (E) Spatial transcription slices showed the spatial expression of SUSD3, ANXA1, and CCR7 markers, and the color of the dots indicated the expression level of the markers.

    Article Snippet: Additionally, the spatial distribution of SUSD3 was examined using the STOmics DB transcription database ( https://db.cngb.org/stomics/ ) ( ).

    Techniques: Single-cell Analysis, Expressing