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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/bulk+data+transcriptomics/bio_rxiv__64898__2026__03__14__711768-36-0-6
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bulk transcriptomics data - by Bioz Stars, 2026-11
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bulk transcriptome data  (Mendeley Ltd)
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Mendeley Ltd bulk transcriptome 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 Transcriptome Data, supplied by Mendeley Ltd, 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/bulk+data+transcriptome/pm41714703-408-1-7
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bulk transcriptome data - by Bioz Stars, 2026-11
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raw bulk transcriptome counts data and clinicopathological information of colon adenocarcinoma (coad) and rectum adenocarcinoma (read)  (Santa Cruz Biotechnology)
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Santa Cruz Biotechnology raw bulk transcriptome counts data and clinicopathological information of colon adenocarcinoma (coad) and rectum adenocarcinoma (read)
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.
Raw Bulk Transcriptome Counts Data And Clinicopathological Information Of Colon Adenocarcinoma (Coad) And Rectum Adenocarcinoma (Read), supplied by Santa Cruz Biotechnology, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bulk+transcriptomics+data/raw+bulk+transcriptome+counts+data+and+clinicopathological+information+of+colon+adenocarcinoma++coad++and+rectum+adenocarcinoma++read+/pmc11335547-39-2-27
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raw bulk transcriptome counts data and clinicopathological information of colon adenocarcinoma (coad) and rectum adenocarcinoma (read) - by Bioz Stars, 2026-11
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bulk whole blood transcriptomics data illumina hiseq2000  (Illumina Inc)
90
Illumina Inc bulk whole blood transcriptomics data illumina hiseq2000
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 .
Bulk Whole Blood Transcriptomics Data Illumina Hiseq2000, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bulk+transcriptomics+data/sequencing+platform/pmc10994553-242-19-24
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bulk whole blood transcriptomics data illumina hiseq2000 - by Bioz Stars, 2026-11
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bulk transcriptomic data  (Human Protein Atlas)
90
Human Protein Atlas bulk transcriptomic data
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 .
Bulk Transcriptomic Data, supplied by Human Protein Atlas, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bulk+transcriptomics+data/transcriptomic+data/pmc10933257-155-12-18
Average 90 stars, based on 1 article reviews
bulk transcriptomic data - by Bioz Stars, 2026-11
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bulk transcriptome data  (Biotechnology Information)
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Biotechnology Information bulk transcriptome data
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 .
Bulk Transcriptome Data, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bulk+transcriptomics+data/raw+transcriptome+data/pmc10693608__41467_2023_43758_MOESM7_ESM-32-8-20
Average 90 stars, based on 1 article reviews
bulk transcriptome data - by Bioz Stars, 2026-11
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bulk transcriptomic data  (Broad Institute Inc)
90
Broad Institute Inc bulk transcriptomic data
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 .
Bulk Transcriptomic Data, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bulk+transcriptomics+data/transcriptome+data/pmc08971486__41467_2022_29358_MOESM2_ESM-360-5-25
Average 90 stars, based on 1 article reviews
bulk transcriptomic data - by Bioz Stars, 2026-11
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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