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spatial transcriptome rna sequencing (strna-seq) data for breast cancer samples, specific to the 10x visium platform  (10X Genomics)

 
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    10X Genomics spatial transcriptome rna sequencing (strna-seq) data for breast cancer samples, specific to the 10x visium platform
    Spatial Transcriptome Rna Sequencing (Strna Seq) Data For Breast Cancer Samples, Specific To The 10x Visium Platform, 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/spatial+transcriptomics+rna+seq+data/cells+generated+with+the+10x+genomics+3%E2%80%99+v+2+protocol/pm38450906-83-9-20
    Average 90 stars, based on 1 article reviews
    spatial transcriptome rna sequencing (strna-seq) data for breast cancer samples, specific to the 10x visium platform - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: Inhibiting EZH2 complements steroid effects in Duchenne muscular dystrophy
    Article Snippet: .. All 10x snRNA-seq, 10x Genomics Visium, and Vizgen MERFISH spatial transcriptomics data used in the study are available at the Korea BioData Station with accession numbers temp-grp-2-1711599940117 , temp-grp-2-1711600165042 , and temp-grp-2-1711600348912 , respectively, with minimal restriction posed by the depository. ..

    Article Title: SpatialQC: automated quality control for spatial transcriptome data
    Article Snippet: .. While several QC tools such as scQCEA ( Nassiri et al. 2023 ) and popsicleR ( Grandi et al. 2022 ) have been developed for scRNA-seq data primarily from the 10X Genomics Cell Ranger pipeline, there is currently a gap in QC software tailored for spatial transcriptomics: (i) abnormal spatial distribution of data quality, which existing methods fail to detect; (ii) assessment of quality at the level of tissue sections, which existing methods cannot accomplish; (iii) variance in sequencing depth across different spatial transcriptomics platforms, requiring quantifiable standards for parameterization, which is more pronounced than for single-cell data. ..

    Software:

    Article Title: SpatialQC: automated quality control for spatial transcriptome data
    Article Snippet: .. While several QC tools such as scQCEA ( Nassiri et al. 2023 ) and popsicleR ( Grandi et al. 2022 ) have been developed for scRNA-seq data primarily from the 10X Genomics Cell Ranger pipeline, there is currently a gap in QC software tailored for spatial transcriptomics: (i) abnormal spatial distribution of data quality, which existing methods fail to detect; (ii) assessment of quality at the level of tissue sections, which existing methods cannot accomplish; (iii) variance in sequencing depth across different spatial transcriptomics platforms, requiring quantifiable standards for parameterization, which is more pronounced than for single-cell data. ..

    Sequencing:

    Article Title: SpatialQC: automated quality control for spatial transcriptome data
    Article Snippet: .. While several QC tools such as scQCEA ( Nassiri et al. 2023 ) and popsicleR ( Grandi et al. 2022 ) have been developed for scRNA-seq data primarily from the 10X Genomics Cell Ranger pipeline, there is currently a gap in QC software tailored for spatial transcriptomics: (i) abnormal spatial distribution of data quality, which existing methods fail to detect; (ii) assessment of quality at the level of tissue sections, which existing methods cannot accomplish; (iii) variance in sequencing depth across different spatial transcriptomics platforms, requiring quantifiable standards for parameterization, which is more pronounced than for single-cell data. ..

    Single Cell:

    Article Title: SpatialQC: automated quality control for spatial transcriptome data
    Article Snippet: .. While several QC tools such as scQCEA ( Nassiri et al. 2023 ) and popsicleR ( Grandi et al. 2022 ) have been developed for scRNA-seq data primarily from the 10X Genomics Cell Ranger pipeline, there is currently a gap in QC software tailored for spatial transcriptomics: (i) abnormal spatial distribution of data quality, which existing methods fail to detect; (ii) assessment of quality at the level of tissue sections, which existing methods cannot accomplish; (iii) variance in sequencing depth across different spatial transcriptomics platforms, requiring quantifiable standards for parameterization, which is more pronounced than for single-cell data. ..



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