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bulk rna-seq eqtl datasets  (Metabrain Research)
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Metabrain Research bulk rna-seq eqtl datasets
Graphical overview of the PICALO method. A PICALO takes <t>eQTL</t> data (i.e., gene expression and genotype dosage values) as input. B Map interactions with a starting position representing an initial guess of biological/technical context. C The starting position is optimized by maximizing the joint log-likelihood on a per-sample basis over multiple ieQTLs. D Mapping of the interactions and the subsequent optimization are repeated until convergence. The influence of the resulting principal interacting component (PIC) is regressed out from the gene expression data, and the process is repeated until no additional PICs and ieQTLs are identified. The resulting PICs capture technical and biological contexts such as cell type proportions. The illustrations shown in A, B and C are generated using dummy data
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Graphical overview of the PICALO method. A PICALO takes eQTL data (i.e., gene expression and genotype dosage values) as input. B Map interactions with a starting position representing an initial guess of biological/technical context. C The starting position is optimized by maximizing the joint log-likelihood on a per-sample basis over multiple ieQTLs. D Mapping of the interactions and the subsequent optimization are repeated until convergence. The influence of the resulting principal interacting component (PIC) is regressed out from the gene expression data, and the process is repeated until no additional PICs and ieQTLs are identified. The resulting PICs capture technical and biological contexts such as cell type proportions. The illustrations shown in A, B and C are generated using dummy data

Journal: Genome Biology

Article Title: PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

doi: 10.1186/s13059-023-03151-0

Figure Lengend Snippet: Graphical overview of the PICALO method. A PICALO takes eQTL data (i.e., gene expression and genotype dosage values) as input. B Map interactions with a starting position representing an initial guess of biological/technical context. C The starting position is optimized by maximizing the joint log-likelihood on a per-sample basis over multiple ieQTLs. D Mapping of the interactions and the subsequent optimization are repeated until convergence. The influence of the resulting principal interacting component (PIC) is regressed out from the gene expression data, and the process is repeated until no additional PICs and ieQTLs are identified. The resulting PICs capture technical and biological contexts such as cell type proportions. The illustrations shown in A, B and C are generated using dummy data

Article Snippet: We made use of the bulk RNA-seq eQTL datasets collected by BIOS [ ] (peripheral blood; n = 3997) and MetaBrain [ ] (multiple brain regions; n = 8727).

Techniques: Gene Expression, Generated

A Pearson correlation heatmap correlating PICs to measured cell type proportions in the blood. The correlation p -values are corrected for multiple testing with Benjamini-Hochberg, and only correlations with an FDR < 0.05 are shown. B Regression plot showing the correlation between PIC2 and myeloid lineage cell proportions (granulocyte + monocyte) in the blood. C Simplified overview of the blood cell type lineage with annotations of PICs describing distinct (groups of) cell types using measured cell type proportions, gene set enrichments, and single-cell expression enrichment. Positive and negative signs indicate the direction of the effect. Only the first 10 PICs are considered. An image of the top layer cell type is created with BioRender.com . D Negatively correlating eQTL genes interacting with PIC10 showed enrichment for type II interferon signaling as annotated by the Interferome Database Annotation

Journal: Genome Biology

Article Title: PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs

doi: 10.1186/s13059-023-03151-0

Figure Lengend Snippet: A Pearson correlation heatmap correlating PICs to measured cell type proportions in the blood. The correlation p -values are corrected for multiple testing with Benjamini-Hochberg, and only correlations with an FDR < 0.05 are shown. B Regression plot showing the correlation between PIC2 and myeloid lineage cell proportions (granulocyte + monocyte) in the blood. C Simplified overview of the blood cell type lineage with annotations of PICs describing distinct (groups of) cell types using measured cell type proportions, gene set enrichments, and single-cell expression enrichment. Positive and negative signs indicate the direction of the effect. Only the first 10 PICs are considered. An image of the top layer cell type is created with BioRender.com . D Negatively correlating eQTL genes interacting with PIC10 showed enrichment for type II interferon signaling as annotated by the Interferome Database Annotation

Article Snippet: We made use of the bulk RNA-seq eQTL datasets collected by BIOS [ ] (peripheral blood; n = 3997) and MetaBrain [ ] (multiple brain regions; n = 8727).

Techniques: Expressing