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Decon Laboratories decon-eqtl
Decon Eqtl, supplied by Decon Laboratories, 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/decon-eqtl/decon+eqtl/pm39824189-24-14-3
Average 90 stars, based on 1 article reviews
decon-eqtl - by Bioz Stars, 2026-09
90/100 stars

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Related Articles

Comparison:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Expressing:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Marker:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

RNA Sequencing:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Purification:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Gene Expression:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Biomarker Discovery:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Derivative Assay:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Cell Counting:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.

Introduce:


Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Secondly, Decon-eQTL is the first approach in which all major cell proportions (the major cell types for which the sum of proportions per sample is approximately 100%) of bulk blood tissue are incorporated into an eQTL model simultaneously.

Article Title: Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Article Snippet: Sheet cortex: All Decon-eQTL results for cortex.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For each of the six cell subpopulations we evaluated in Decon-eQTL, their CTi eQTL genes had a significantly higher correlation with their relevant cell subpopulation than with other cell types (T-test, p -value < 0.05) (Fig. b).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Finally, for granulocytes, CD4+ T cell eQTLs and monocytes, we overlapped the results from Westra method and Decon-eQTL with the eQTLs from purified cell types (Chen et al [ ]) (Supplementary Fig. ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: For CD4+ T-cells, Decon-eQTL had a better replication rate (p-value = 7.47 × 10 − 12 ).

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: Supplementary Figure 17 Comparison of Decon-eQTL with Westra et al method.

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Article Snippet: In the Westra et al. model, expression data is normalized in the same way as in Decon-eQTL.



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( A ) Total <t>SCZ</t> <t>GWAS</t> <t>heritability</t> ( h 2) explained by eQTLs. ( B ) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h 2/number of SNPs in each eQTL category.
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( A ) Illustration of decon-eQTL mapping. ( B ) Number of <t>decon-eQTLs</t> identified in different cell types at FDR < 0.05 in the permutation test. ( C ) Pi 1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and ( D ) eQTLs from snRNA-seq study of Bryois et al. . For calculating Pi 1 statistics, decon-eQTLs from ROSMAP were used as testing data, and eQTLs from BrainGVEX and Bryois et al. were used as references. ( E ) Comparison of decon-eQTLs and bulk tissue eQTLs. The top barplot shows the Pi 1 values of decon-eQTLs (testing data) in bulk tissue eQTLs (reference). The bottom plot shows the intersections between decon-eQTLs and bulk tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.
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( A ) Illustration of decon-eQTL mapping. ( B ) Number of <t>decon-eQTLs</t> identified in different cell types at FDR < 0.05 in the permutation test. ( C ) Pi 1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and ( D ) eQTLs from snRNA-seq study of Bryois et al. . For calculating Pi 1 statistics, decon-eQTLs from ROSMAP were used as testing data, and eQTLs from BrainGVEX and Bryois et al. were used as references. ( E ) Comparison of decon-eQTLs and bulk tissue eQTLs. The top barplot shows the Pi 1 values of decon-eQTLs (testing data) in bulk tissue eQTLs (reference). The bottom plot shows the intersections between decon-eQTLs and bulk tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.
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Average 90 stars, based on 1 article reviews
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Image Search Results


( A ) Total SCZ GWAS heritability ( h 2) explained by eQTLs. ( B ) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h 2/number of SNPs in each eQTL category.

Journal: Science Advances

Article Title: Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

doi: 10.1126/sciadv.adh2588

Figure Lengend Snippet: ( A ) Total SCZ GWAS heritability ( h 2) explained by eQTLs. ( B ) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h 2/number of SNPs in each eQTL category.

Article Snippet: Decon-eQTLs of all cell types were enriched for SCZ GWAS heritability ( P value < 0.05; ).

Techniques:

( A ) Illustration of decon-eQTL mapping. ( B ) Number of decon-eQTLs identified in different cell types at FDR < 0.05 in the permutation test. ( C ) Pi 1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and ( D ) eQTLs from snRNA-seq study of Bryois et al. . For calculating Pi 1 statistics, decon-eQTLs from ROSMAP were used as testing data, and eQTLs from BrainGVEX and Bryois et al. were used as references. ( E ) Comparison of decon-eQTLs and bulk tissue eQTLs. The top barplot shows the Pi 1 values of decon-eQTLs (testing data) in bulk tissue eQTLs (reference). The bottom plot shows the intersections between decon-eQTLs and bulk tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.

Journal: Science Advances

Article Title: Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

doi: 10.1126/sciadv.adh2588

Figure Lengend Snippet: ( A ) Illustration of decon-eQTL mapping. ( B ) Number of decon-eQTLs identified in different cell types at FDR < 0.05 in the permutation test. ( C ) Pi 1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and ( D ) eQTLs from snRNA-seq study of Bryois et al. . For calculating Pi 1 statistics, decon-eQTLs from ROSMAP were used as testing data, and eQTLs from BrainGVEX and Bryois et al. were used as references. ( E ) Comparison of decon-eQTLs and bulk tissue eQTLs. The top barplot shows the Pi 1 values of decon-eQTLs (testing data) in bulk tissue eQTLs (reference). The bottom plot shows the intersections between decon-eQTLs and bulk tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.

Article Snippet: The cell-type eQTLs identified with deconvoluted gene expressions were named deconvolution eQTLs (decon-eQTLs).

Techniques: Comparison

( A ) Total SCZ GWAS heritability ( h 2) explained by eQTLs. ( B ) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h 2/number of SNPs in each eQTL category.

Journal: Science Advances

Article Title: Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

doi: 10.1126/sciadv.adh2588

Figure Lengend Snippet: ( A ) Total SCZ GWAS heritability ( h 2) explained by eQTLs. ( B ) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h 2/number of SNPs in each eQTL category.

Article Snippet: The cell-type eQTLs identified with deconvoluted gene expressions were named deconvolution eQTLs (decon-eQTLs).

Techniques: