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scatac seq data  (10X Genomics)

 
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    10X Genomics scatac seq data
    Scatac Seq Data, supplied by 10X Genomics, 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/scatac+seq+data/data+scrna+seq/pmc13192178-345-3-56
    Average 86 stars, based on 1 article reviews
    scatac seq data - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    Activity Assay:

    Article Title: A mouse tissue atlas of small noncoding RNA
    Article Snippet: .. To compute the accessibility scores for the brain-specific ncRNA in Fig. 6 H , we used Cicero to derive gene activity scores from scATAC-seq data generated by 10XGenomics for the mouse adult brain ( https://www.10xgenomics.com/10x-university/single-cell-atac/ ) with the chromium single-cell ATAC platform, and demultiplexed and preprocessed with the single-cell ATAC Cell Ranger platform. ..

    Generated:

    Article Title: A mouse tissue atlas of small noncoding RNA
    Article Snippet: .. To compute the accessibility scores for the brain-specific ncRNA in Fig. 6 H , we used Cicero to derive gene activity scores from scATAC-seq data generated by 10XGenomics for the mouse adult brain ( https://www.10xgenomics.com/10x-university/single-cell-atac/ ) with the chromium single-cell ATAC platform, and demultiplexed and preprocessed with the single-cell ATAC Cell Ranger platform. ..

    Gene Expression:

    Article Title: Flexible modeling of regulatory networks improves transcription factor activity estimation.
    Article Snippet: .. TIGER improves identification of cell type-specific TF regulators Since TIGER is able to refine the regulatory network to reflect the cellular context, we applied TIGER and all other methods to only the scRNA-seq data from the 10x Genomics PBMC10K multiome dataset, screening out the scATAC-seq data completely, and asked which method was best able to infer TFA from the gene expression data and DoRothEA prior. ..

    Article Title: Flexible modeling of regulatory networks improves transcription factor activity estimation
    Article Snippet: .. Since TIGER is able to refine the regulatory network to reflect the cellular context, we applied TIGER and all other methods to only the scRNA-seq data from the 10x Genomics PBMC10K multiome dataset, screening out the scATAC-seq data completely, and asked which method was best able to infer TFA from the gene expression data and DoRothEA prior. ..

    Article Title: Benchmarking component choices for unpaired single cell RNA and epigenomic integration
    Article Snippet: .. The scRNA-seq and scATAC-seq data from human peripheral blood mononuclear cells (PBMC) [ ], human brain [ ], fresh embryonic E18 mouse brain [ ], and mouse kidney cancer [ ], human kidney cancer [ ], human small intestine [ ] were obtained from the 10 × Genomics Multiome ATAC + Gene Expression dataset portal. ( https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets ). ..

    Derivative Assay:

    Article Title: InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop
    Article Snippet: .. First, we use InferLoop to predict cell-type-specific loop signals from the scATAC-seq data of human frontal cortex [ ] and human blood (10x Genomic Multiome dataset), and compare the predicted signals with the real signals derived from the matched single-cell 3D genome structure data [ , ]. ..

    Single Cell:

    Article Title: InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop
    Article Snippet: .. First, we use InferLoop to predict cell-type-specific loop signals from the scATAC-seq data of human frontal cortex [ ] and human blood (10x Genomic Multiome dataset), and compare the predicted signals with the real signals derived from the matched single-cell 3D genome structure data [ , ]. ..



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    Single-cell multiomic characterization of a differentiation-stalled state in ATRA-treated HL-60 cells. A . UMAP visualization <t>of</t> <t>scATAC-seq</t> data from HL-60 cells treated with ATRA for 0, 1, 3, or 6 days, colored by three identified clusters. C1 represents naïve cells; C2 and C3 represent ATRA-induced cell clusters. B . Heatmap showing the relative proportion of cells from each treatment time point within clusters C1–C3. C . Bar plot showing predicted lineage identity of each cluster, as determined by projection onto a reference atlas of normal human hematopoiesis. Each color represents a distinct hematopoietic lineage. D . Heatmap of scores of peak-to-gene (P2G) linkages derived from the integrated scATAC-seq and scRNA-seq data across the three clusters. E . GO enrichment analysis of featured P2G-linked genes. F . Feature plots from integrated scATAC-seq and scRNA-seq data. Top panels show gene activity from scATAC-seq; bottom panels show matched gene expression from scRNA-seq
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    Single-cell multiomic characterization of a differentiation-stalled state in ATRA-treated HL-60 cells. A . UMAP visualization <t>of</t> <t>scATAC-seq</t> data from HL-60 cells treated with ATRA for 0, 1, 3, or 6 days, colored by three identified clusters. C1 represents naïve cells; C2 and C3 represent ATRA-induced cell clusters. B . Heatmap showing the relative proportion of cells from each treatment time point within clusters C1–C3. C . Bar plot showing predicted lineage identity of each cluster, as determined by projection onto a reference atlas of normal human hematopoiesis. Each color represents a distinct hematopoietic lineage. D . Heatmap of scores of peak-to-gene (P2G) linkages derived from the integrated scATAC-seq and scRNA-seq data across the three clusters. E . GO enrichment analysis of featured P2G-linked genes. F . Feature plots from integrated scATAC-seq and scRNA-seq data. Top panels show gene activity from scATAC-seq; bottom panels show matched gene expression from scRNA-seq
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    Image Search Results


    Single-cell multiomic characterization of a differentiation-stalled state in ATRA-treated HL-60 cells. A . UMAP visualization of scATAC-seq data from HL-60 cells treated with ATRA for 0, 1, 3, or 6 days, colored by three identified clusters. C1 represents naïve cells; C2 and C3 represent ATRA-induced cell clusters. B . Heatmap showing the relative proportion of cells from each treatment time point within clusters C1–C3. C . Bar plot showing predicted lineage identity of each cluster, as determined by projection onto a reference atlas of normal human hematopoiesis. Each color represents a distinct hematopoietic lineage. D . Heatmap of scores of peak-to-gene (P2G) linkages derived from the integrated scATAC-seq and scRNA-seq data across the three clusters. E . GO enrichment analysis of featured P2G-linked genes. F . Feature plots from integrated scATAC-seq and scRNA-seq data. Top panels show gene activity from scATAC-seq; bottom panels show matched gene expression from scRNA-seq

    Journal: Journal of Translational Medicine

    Article Title: mTOR inhibition promotes ATRA-induced cancer cell differentiation by overcoming a metabolic hyperactive state

    doi: 10.1186/s12967-026-08218-7

    Figure Lengend Snippet: Single-cell multiomic characterization of a differentiation-stalled state in ATRA-treated HL-60 cells. A . UMAP visualization of scATAC-seq data from HL-60 cells treated with ATRA for 0, 1, 3, or 6 days, colored by three identified clusters. C1 represents naïve cells; C2 and C3 represent ATRA-induced cell clusters. B . Heatmap showing the relative proportion of cells from each treatment time point within clusters C1–C3. C . Bar plot showing predicted lineage identity of each cluster, as determined by projection onto a reference atlas of normal human hematopoiesis. Each color represents a distinct hematopoietic lineage. D . Heatmap of scores of peak-to-gene (P2G) linkages derived from the integrated scATAC-seq and scRNA-seq data across the three clusters. E . GO enrichment analysis of featured P2G-linked genes. F . Feature plots from integrated scATAC-seq and scRNA-seq data. Top panels show gene activity from scATAC-seq; bottom panels show matched gene expression from scRNA-seq

    Article Snippet: Integration of scATAC-seq with scRNA-seq data (HL-60 cells under ATRA time course, from National Omics Data Encyclopedia, project ID OEP00001921) was performed using a constrained integration strategy in ArchR, in both scRNA-seq and scATAC-seq data, clusters corresponding to the time of collection (i.e., ATRA_day0, ATRA_day1, ATRA_day3, or ATRA_day6) were specified.

    Techniques: Single Cell, Derivative Assay, Activity Assay, Gene Expression