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cell rna seq dataset  (Broad Clinical Labs)


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    Broad Clinical Labs cell rna seq dataset
    Cell Rna Seq Dataset, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 723 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/single+cell+rna+seq+datasets/pm42120390-512-1-13?v=Broad+Clinical+Labs
    Average 96 stars, based on 723 article reviews
    cell rna seq dataset - by Bioz Stars, 2026-07
    96/100 stars

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    <t>Single-cell</t> <t>transcriptomic</t> analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts <t>(nFeature_RNA),</t> UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell <t>RNA-seq</t> data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.
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    Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

    Journal: Frontiers in Immunology

    Article Title: Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

    doi: 10.3389/fimmu.2026.1705706

    Figure Lengend Snippet: Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

    Article Snippet: Single-cell RNA sequencing (scRNA-seq) datasets were obtained from GSE145086 and GSE233084 , both generated using the 10X Genomics platform ( , ).

    Techniques: Single Cell, Control, RNA Sequencing, Cell Cycle Assay, Expressing