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scrnaseq datasets  (10X Genomics)

 
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    Structured Review

    10X Genomics scrnaseq datasets
    Scrnaseq Datasets, 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/scrnaseq+dataset/datasets+scrnaseq/pmc13198004-370-0-10
    Average 86 stars, based on 1 article reviews
    scrnaseq datasets - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    Isolation:

    Article Title: Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies.
    Article Snippet: .. Top right: CellTypist annotation of scRNAseq dataset with the “Immune_All_Low” model. Bottom left: CellTypist annotation of scRNAseq dataset with the “COVID19_Immune_Landscape” model. Bottom Right: CellTypist annotation of scRNAseq dataset with the “Healthy_COVID19_PBMC” model. (d) UMAP projection of scRNAseq dataset with PC39-1 bnAb lineage members colored in red. (e) Frequency of each cell type at each indicated timepoint post-infection within PC39-1 lineage members isolated with 10X Genomics. (TIF) S8 Fig. PC39-1 bnAb lineage members show little auto-/poly-reactivity. (a) Binding ELISA curves generated with the indicated antibodies and autoantigens. (b) PC39-1 Ab binding EC50 values (μg/mL) to the indicated autoantigens. ..

    Binding Assay:

    Article Title: Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies.
    Article Snippet: .. Top right: CellTypist annotation of scRNAseq dataset with the “Immune_All_Low” model. Bottom left: CellTypist annotation of scRNAseq dataset with the “COVID19_Immune_Landscape” model. Bottom Right: CellTypist annotation of scRNAseq dataset with the “Healthy_COVID19_PBMC” model. (d) UMAP projection of scRNAseq dataset with PC39-1 bnAb lineage members colored in red. (e) Frequency of each cell type at each indicated timepoint post-infection within PC39-1 lineage members isolated with 10X Genomics. (TIF) S8 Fig. PC39-1 bnAb lineage members show little auto-/poly-reactivity. (a) Binding ELISA curves generated with the indicated antibodies and autoantigens. (b) PC39-1 Ab binding EC50 values (μg/mL) to the indicated autoantigens. ..

    Enzyme-linked Immunosorbent Assay:

    Article Title: Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies.
    Article Snippet: .. Top right: CellTypist annotation of scRNAseq dataset with the “Immune_All_Low” model. Bottom left: CellTypist annotation of scRNAseq dataset with the “COVID19_Immune_Landscape” model. Bottom Right: CellTypist annotation of scRNAseq dataset with the “Healthy_COVID19_PBMC” model. (d) UMAP projection of scRNAseq dataset with PC39-1 bnAb lineage members colored in red. (e) Frequency of each cell type at each indicated timepoint post-infection within PC39-1 lineage members isolated with 10X Genomics. (TIF) S8 Fig. PC39-1 bnAb lineage members show little auto-/poly-reactivity. (a) Binding ELISA curves generated with the indicated antibodies and autoantigens. (b) PC39-1 Ab binding EC50 values (μg/mL) to the indicated autoantigens. ..

    Generated:

    Article Title: Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies.
    Article Snippet: .. Top right: CellTypist annotation of scRNAseq dataset with the “Immune_All_Low” model. Bottom left: CellTypist annotation of scRNAseq dataset with the “COVID19_Immune_Landscape” model. Bottom Right: CellTypist annotation of scRNAseq dataset with the “Healthy_COVID19_PBMC” model. (d) UMAP projection of scRNAseq dataset with PC39-1 bnAb lineage members colored in red. (e) Frequency of each cell type at each indicated timepoint post-infection within PC39-1 lineage members isolated with 10X Genomics. (TIF) S8 Fig. PC39-1 bnAb lineage members show little auto-/poly-reactivity. (a) Binding ELISA curves generated with the indicated antibodies and autoantigens. (b) PC39-1 Ab binding EC50 values (μg/mL) to the indicated autoantigens. ..

    Immunopeptidomics:

    Article Title: Antigen pressure from two founder viruses induces multiple insertions at a single antibody position to generate broadly neutralizing HIV antibodies.
    Article Snippet: .. Top right: CellTypist annotation of scRNAseq dataset with the “Immune_All_Low” model. Bottom left: CellTypist annotation of scRNAseq dataset with the “COVID19_Immune_Landscape” model. Bottom Right: CellTypist annotation of scRNAseq dataset with the “Healthy_COVID19_PBMC” model. (d) UMAP projection of scRNAseq dataset with PC39-1 bnAb lineage members colored in red. (e) Frequency of each cell type at each indicated timepoint post-infection within PC39-1 lineage members isolated with 10X Genomics. (TIF) S8 Fig. PC39-1 bnAb lineage members show little auto-/poly-reactivity. (a) Binding ELISA curves generated with the indicated antibodies and autoantigens. (b) PC39-1 Ab binding EC50 values (μg/mL) to the indicated autoantigens. ..



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    A . Total transcript (UMI) counts per cell N i in a combined E18 mouse brain dataset from <t>10X</t> Genomics (see Methods). Each data point represents an individual cell. B . Weight distribution coefficient ( WDC ) for the statistical weight of different mRNA transcripts (see Methods). Each point shows WDC z for a gene Z calculated directly from the measured transcript counts as described in Supplement 1. Standard, unweighted averaging of transcript counts utilized in most DGE analysis models assumes WDC z = 0. Utilization of aggregate counts in pseudo-bulk models assumes WDC z = 1. Neither reflects the reality.
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    Image Search Results


    A . Total transcript (UMI) counts per cell N i in a combined E18 mouse brain dataset from 10X Genomics (see Methods). Each data point represents an individual cell. B . Weight distribution coefficient ( WDC ) for the statistical weight of different mRNA transcripts (see Methods). Each point shows WDC z for a gene Z calculated directly from the measured transcript counts as described in Supplement 1. Standard, unweighted averaging of transcript counts utilized in most DGE analysis models assumes WDC z = 0. Utilization of aggregate counts in pseudo-bulk models assumes WDC z = 1. Neither reflects the reality.

    Journal: bioRxiv

    Article Title: Differential expression analysis in single cell and spatial RNASeq without model assumptions

    doi: 10.1101/2025.10.20.683496

    Figure Lengend Snippet: A . Total transcript (UMI) counts per cell N i in a combined E18 mouse brain dataset from 10X Genomics (see Methods). Each data point represents an individual cell. B . Weight distribution coefficient ( WDC ) for the statistical weight of different mRNA transcripts (see Methods). Each point shows WDC z for a gene Z calculated directly from the measured transcript counts as described in Supplement 1. Standard, unweighted averaging of transcript counts utilized in most DGE analysis models assumes WDC z = 0. Utilization of aggregate counts in pseudo-bulk models assumes WDC z = 1. Neither reflects the reality.

    Article Snippet: For simplicity and consistency, we compare the results produced by our method with the standard models within the Seurat workflow, using publicly available 10X Genomics scRNASeq datasets ( https://www.10xgenomics.com/datasets ) and our spRNASeq data collected with the 10X Genomics Visium HD assay.

    Techniques: