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rna seq datasets  (Human Brain Project)

 
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    Human Brain Project rna seq datasets
    Rna Seq Datasets, supplied by Human Brain Project, 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/rna+seq+datasets/datasets+rna+seq/pm42129508-31-0-6
    Average 86 stars, based on 1 article reviews
    rna seq datasets - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    RNA Sequencing:

    Article Title: Spatiotemporal brain transcriptomics reveal risk gene hot-spots in major neuropsychiatric disorders.
    Article Snippet: .. RNA-seq datasets from the BrainSpan developmental human brain project, Allen Human Brain Atlas (AHBA) microarray data, and single-cell RNA-seq data were integrated into the analysis pipeline. ..

    Microarray:

    Article Title: Spatiotemporal brain transcriptomics reveal risk gene hot-spots in major neuropsychiatric disorders.
    Article Snippet: .. RNA-seq datasets from the BrainSpan developmental human brain project, Allen Human Brain Atlas (AHBA) microarray data, and single-cell RNA-seq data were integrated into the analysis pipeline. ..

    Single Cell:

    Article Title: Spatiotemporal brain transcriptomics reveal risk gene hot-spots in major neuropsychiatric disorders.
    Article Snippet: .. RNA-seq datasets from the BrainSpan developmental human brain project, Allen Human Brain Atlas (AHBA) microarray data, and single-cell RNA-seq data were integrated into the analysis pipeline. ..



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    Image Search Results


    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