spatial transcriptome rna sequencing (strna-seq) data for breast cancer samples, specific to the 10x visium platform (10X Genomics)
90
Structured Review
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/visium+spatial+sequencing+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, 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/visium+spatial+sequencing+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
Images
Related Articles
RNA Sequencing:Article Title: A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data Article Snippet: .. We downloaded RNA-seq bam files of the other:Article Title: CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data. Article Snippet: Summary of the simulated scRNA-seq datasets Dataset name Cell type Subtype number Gene number Cell number Sequencing platform PBMC [29] Peripheral blood mononuclear cells 8 16449 4271 Expressing:Article Title: Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer Article Snippet: .. 8 ● Differential transcript usage, differential transcript expression and differential gene expression analysis ○ For the cell line gene expression analysis, in addition to good library-level correlation of gene quantitation between targeted short- and long-read methods (Supplementary Fig. 4), we observe good concordance in DEGs among matched individual cell lines across both methods: ○ The 10x Genomics Gene Expression:Article Title: Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer Article Snippet: .. 8 ● Differential transcript usage, differential transcript expression and differential gene expression analysis ○ For the cell line gene expression analysis, in addition to good library-level correlation of gene quantitation between targeted short- and long-read methods (Supplementary Fig. 4), we observe good concordance in DEGs among matched individual cell lines across both methods: ○ The 10x Genomics Article Title: Benchmarking computational methods for single-cell chromatin data analysis Article Snippet: .. For the unprocessed data, fragment files of the cell line dataset were downloaded from GEO accession GSE162690 [ ], Quantitation Assay:Article Title: Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer Article Snippet: .. 8 ● Differential transcript usage, differential transcript expression and differential gene expression analysis ○ For the cell line gene expression analysis, in addition to good library-level correlation of gene quantitation between targeted short- and long-read methods (Supplementary Fig. 4), we observe good concordance in DEGs among matched individual cell lines across both methods: ○ The 10x Genomics Formalin-fixed Paraffin-Embedded:Article Title: Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics Article Snippet: .. Formalin-fixed, paraffin-embedded (FFPE) human pancreatic cancer tissue sections were placed on spatial Sequencing:Article Title: Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics Article Snippet: .. Formalin-fixed, paraffin-embedded (FFPE) human pancreatic cancer tissue sections were placed on spatial |
![A , B Spatial plot of seven cell types estimated by cell2location (spot resolution) and FineST (single-cell resolution). A' , B' Zoon-in views of A and B . B'' Zoon-in view of the region marked in B' , where FineST increases resolution from 8 spots to 869 single cells. C Cell type composition from reference scRNA-seq and deconvolution at Visium spot and FineST single-cell levels. D Distribution and PCC of cell type proportions ( n = 7) for Visium spot (PCC = 0.24) and FineST single-cell (PCC = 0.64) resolutions vs reference scRNA-seq. E Pathologists identified 36 spots (of 1331) co-localized with tertiary lymphoid structure (TLS), important for antigen presentation and T cell activation. FineST's single-cell views validate TLS by T and B cell co-localization (see B'' ). F Three example spatially co-expressed LR pairs detected at FineST's single-cell resolution. G Visualization of PVR - TIGIT interaction among local single cells ( z -score FDR < 0.05). H Clustering of 633 significant LR pairs, from single-cell resolution, within selected ROI into three spatial patterns using SpatialDE. I Spatial co-localization of tumor and Treg cells, estimated by cell2location . J Spatially co-expressed LR pairs detected at FineST's single-cell resolution. Scatter plot of global Moran’s R and one-sided z -score p -values (orange: significant, FDR < 0.05, Benjamini-Hochberg correction), with examples highlighted. K Communication strength of CD70 - CD27 at single-cell resolution (color: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{p}_{{{{\rm{local}}}}{z}_{p}}$$\end{document} 1 − p local z p ; mean strength: 0.45, interacting cells: 5961, occupancy: 49.2%). L Detection of local CCC patterns by clustering significant LR pairs. Top: original spot resolution (332 pairs, SpatialDE); Middle: sub-spot resolution (957 pairs, SparseAEH) and Bottom: single-nucleus resolution (931 pairs, SparseAEH). M Dot plots of enriched pathways in Pattern 0 and Pattern 1 (from L , Bottom), 2-by-2 contingency tables for MHC-I and WNT. Statistical significance was assessed using a one-sided Fisher’s exact test; dot size indicates p -value. N Sankey plot of selected L-R-TF-TG communication pathways. For panels (D , K ), box plots show median (center), IQR (box), whiskers at 1.5 × IQR, and points for seven cell types; violin plots show density, median (white line), and IQR (thick bar). For panels ( A ), ( J , L ), source data are provided in the Source Data file.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_1544/pmc13201544/pmc13201544__41467_2026_70528_Fig4_HTML.jpg)
