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bulk transcriptomics data  (Human Protein Atlas)

 
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    Human Protein Atlas bulk transcriptomics data
    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk <t>transcriptomics</t> data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.
    Bulk Transcriptomics Data, supplied by Human Protein Atlas, 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/bulk+transcriptomics+data/bio_rxiv__64898__2026__03__14__711768-36-0-6?v=Human+Protein+Atlas
    Average 86 stars, based on 1 article reviews
    bulk transcriptomics data - by Bioz Stars, 2026-07
    86/100 stars

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    1) Product Images from "A high-resolution spatial map of cilia-associated proteins in the human fallopian tube"

    Article Title: A high-resolution spatial map of cilia-associated proteins in the human fallopian tube

    Journal: bioRxiv

    doi: 10.64898/2026.03.14.711768

    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.
    Figure Legend Snippet: The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.

    Techniques Used: Expressing, Transcriptomics, Biomarker Discovery



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    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk <t>transcriptomics</t> data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.
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    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk <t>transcriptomics</t> data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.
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    A. Superscores plot based on multi-omics (metabolomics, proteomics, and <t>transcriptomics)</t> pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .
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    Image Search Results


    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.

    Journal: bioRxiv

    Article Title: A high-resolution spatial map of cilia-associated proteins in the human fallopian tube

    doi: 10.64898/2026.03.14.711768

    Figure Lengend Snippet: The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.

    Article Snippet: Bulk transcriptomics data, derived from the Human Protein Atlas (HPA) resource , identified 310 genes with an elevated expression in human FT compared to other tissues and organs in the human body, of which a majority were found to be associated with the structure and function of motile cilia.

    Techniques: Expressing, Transcriptomics, Biomarker Discovery

    A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .

    Journal: PLOS Computational Biology

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

    doi: 10.1371/journal.pcbi.1011814

    Figure Lengend Snippet: A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Biomarker Discovery, Blocking Assay

    Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.

    Journal: PLOS Computational Biology

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

    doi: 10.1371/journal.pcbi.1011814

    Figure Lengend Snippet: Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Biomarker Discovery

    Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.

    Journal: PLOS Computational Biology

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

    doi: 10.1371/journal.pcbi.1011814

    Figure Lengend Snippet: Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Comparison, Biomarker Discovery