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:

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

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:

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Workflow of application of Decon2 to predict cell counts followed by deconvolution of whole blood eQTLs. Using whole blood expression and FACS data of 500FG samples, Decon-cell predicts cell proportions with selected marker genes of circulating immune cell subpopulations. Validations of Decon-cell were carried out on three independent cohorts for which measurements of neutrophils/granulocytes, lymphocytes and monocytes CD14+ were available along with expression profiles of whole blood. Benchmarking of Decon-cell was performed against CIBERSORT and xCell . Decon-cell was applied to an independent cohort (BIOS) to predict cell counts using whole blood RNA-seq. Decon-eQTL subsequently integrates genotype and tissue expression data together with predicted cell proportions for samples in BIOS to detect cell type eQTLs. We validated Decon-eQTL using multiple independent sources, including expression profiles of purified cell subpopulations, eQTLs and chromatin mark QTLs (cmQTLs) from purified neutrophils, monocytes CD14+ and CD4+ T cells , and single-cell eQTL results . Benchmarking of Decon-eQTL was carried out for comparison with a previously reported methods that detected cell type–eQTL effects using whole blood expression data, i.e. the Westra et al.

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Expressing, Marker, RNA Sequencing, Purification, Comparison

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Deconvolution of whole blood eQTLs into CTi eQTLs. Decon-eQTL detects CTi eQTLs by integrating proportions of cell subpopulations (predicted by Decon-cell), gene expression and genotype information. a Number of deconvoluted CTi eQTLs in each cell type using whole blood RNA-seq data of 3189 samples in BIOS cohort. b Distribution of Spearman correlation coefficients between expression levels of CTi eQTL genes and cell counts for each cell subpopulation. The CTi eQTL genes show positive and statistically higher correlation (Spearman) with the relevant cell type proportions as compared to the rest (T-test p -value < 0.05) in an independent cohort (500FG)

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Gene Expression, RNA Sequencing, Expressing

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Validation of CTi eQTLs. a The expression of CTi eQTL genes in purified cell subpopulations from BLUEPRINT are significantly higher in the relevant cell subpopulation when compared to other available cell subtypes (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). b Genes differentially expressed (Adjusted p-value ≤0.5) between CD4+ T cells and NK cells are significantly enriched for CT eQTLs effects on CD4+ T cells (dots in purple, Fisher exact P = 1.8 × 10 17 ) and NK Cells (dots in yellow, Fisher exact P = 2.3 × 10 18 ), respectively. c CTi-eQTLs (FDR ≤ 0.05) show significantly larger effect sizes in the purified cell eQTL data compared to the rest of the whole blood eQTLs for which we do not detect a cell type effect, as shown for deconvoluted granulocyte eQTLs in neutrophil-derived eQTLs (green),monocytes (orange) and CD4+ T cells (purple)

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Biomarker Discovery, Expressing, Purification, Derivative Assay

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Allelic concordance of CTi eQTLs with eQTLs from purified cells. CTi eQTLs show high allelic concordance compared to eQTLs from purified cell subpopulations9. ( a ) for granulocyte eQTLs (green), CTi eQTLs achieved an allelic concordance of 99% compared to eQTLs from purified neutrophils. Similarly, the allelic concordances were 96 and 99% for CD14+ monocytes and CD4+ T cells, respectively. Except for monocytes, these values are higher than those observed for whole blood eQTLs when comparing to eQTLs from purified subpopulations, as shown in panel ( b )

Article Snippet: To validate the CT eQTLs defined by Decon-eQTL, we utilized eQTLs identified from purified neutrophils, CD4+ T-cells and CD14+ monocytes [ ].

Techniques: Purification

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Workflow of application of Decon2 to predict cell counts followed by deconvolution of whole blood eQTLs. Using whole blood expression and FACS data of 500FG samples, Decon-cell predicts cell proportions with selected marker genes of circulating immune cell subpopulations. Validations of Decon-cell were carried out on three independent cohorts for which measurements of neutrophils/granulocytes, lymphocytes and monocytes CD14+ were available along with expression profiles of whole blood. Benchmarking of Decon-cell was performed against CIBERSORT and xCell . Decon-cell was applied to an independent cohort (BIOS) to predict cell counts using whole blood RNA-seq. Decon-eQTL subsequently integrates genotype and tissue expression data together with predicted cell proportions for samples in BIOS to detect cell type eQTLs. We validated Decon-eQTL using multiple independent sources, including expression profiles of purified cell subpopulations, eQTLs and chromatin mark QTLs (cmQTLs) from purified neutrophils, monocytes CD14+ and CD4+ T cells , and single-cell eQTL results . Benchmarking of Decon-eQTL was carried out for comparison with a previously reported methods that detected cell type–eQTL effects using whole blood expression data, i.e. the Westra et al.

Article Snippet: The CTi eQTLs detected by Decon-eQTL tend to be eQTL exclusive for the specific CT, suggesting that the CT with the strongest eQTL effect was selected by Decon-eQTL.

Techniques: Expressing, Marker, RNA Sequencing, Purification, Comparison

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Deconvolution of whole blood eQTLs into CTi eQTLs. Decon-eQTL detects CTi eQTLs by integrating proportions of cell subpopulations (predicted by Decon-cell), gene expression and genotype information. a Number of deconvoluted CTi eQTLs in each cell type using whole blood RNA-seq data of 3189 samples in BIOS cohort. b Distribution of Spearman correlation coefficients between expression levels of CTi eQTL genes and cell counts for each cell subpopulation. The CTi eQTL genes show positive and statistically higher correlation (Spearman) with the relevant cell type proportions as compared to the rest (T-test p -value < 0.05) in an independent cohort (500FG)

Article Snippet: The CTi eQTLs detected by Decon-eQTL tend to be eQTL exclusive for the specific CT, suggesting that the CT with the strongest eQTL effect was selected by Decon-eQTL.

Techniques: Gene Expression, RNA Sequencing, Expressing

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Validation of CTi eQTLs. a The expression of CTi eQTL genes in purified cell subpopulations from BLUEPRINT are significantly higher in the relevant cell subpopulation when compared to other available cell subtypes (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). b Genes differentially expressed (Adjusted p-value ≤0.5) between CD4+ T cells and NK cells are significantly enriched for CT eQTLs effects on CD4+ T cells (dots in purple, Fisher exact P = 1.8 × 10 17 ) and NK Cells (dots in yellow, Fisher exact P = 2.3 × 10 18 ), respectively. c CTi-eQTLs (FDR ≤ 0.05) show significantly larger effect sizes in the purified cell eQTL data compared to the rest of the whole blood eQTLs for which we do not detect a cell type effect, as shown for deconvoluted granulocyte eQTLs in neutrophil-derived eQTLs (green),monocytes (orange) and CD4+ T cells (purple)

Article Snippet: The CTi eQTLs detected by Decon-eQTL tend to be eQTL exclusive for the specific CT, suggesting that the CT with the strongest eQTL effect was selected by Decon-eQTL.

Techniques: Biomarker Discovery, Expressing, Purification, Derivative Assay

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Allelic concordance of CTi eQTLs with eQTLs from purified cells. CTi eQTLs show high allelic concordance compared to eQTLs from purified cell subpopulations9. ( a ) for granulocyte eQTLs (green), CTi eQTLs achieved an allelic concordance of 99% compared to eQTLs from purified neutrophils. Similarly, the allelic concordances were 96 and 99% for CD14+ monocytes and CD4+ T cells, respectively. Except for monocytes, these values are higher than those observed for whole blood eQTLs when comparing to eQTLs from purified subpopulations, as shown in panel ( b )

Article Snippet: The CTi eQTLs detected by Decon-eQTL tend to be eQTL exclusive for the specific CT, suggesting that the CT with the strongest eQTL effect was selected by Decon-eQTL.

Techniques: Purification

Journal: BMC Bioinformatics

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1186/s12859-020-03576-5

Figure Lengend Snippet: Allelic concordance of CTi eQTLs with eQTLs from single cell RNAseq. a Comparison in allelic direction between CTi eQTLs and eQTLs from single cell RNAseq experiments in 6 cell types. b Comparison in allelic direction between Westra model eQTLs and single cell eQTLs. In both panels coloured diamonds are FDR < 0.05, grey circles are FDR > = 0.0 in the single cell data, and the size is the -log10(p-value) of the predicted cell type interacting eQTLs

Article Snippet: The CTi eQTLs detected by Decon-eQTL tend to be eQTL exclusive for the specific CT, suggesting that the CT with the strongest eQTL effect was selected by Decon-eQTL.

Techniques: Comparison

Journal: bioRxiv

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1101/548669

Figure Lengend Snippet: With whole blood expression and FACS data of 500FG samples, Decon-cell predicts cell proportions with selected marker genes of circulating immune cell subpopulations. Validations of Decon-cell were carried out on three independent cohorts where measurements of neutrophils/granulocytes, lymphocytes and monocytes CD14+ were available, alongside to expression profiles of whole blood. Benchmarking of Decon-cell was performed against CIBERSORT and xCell . Decon-cell was applied to an independent cohort (BIOS) to predict cell counts using whole blood RNA-seq. Decon-eQTL subsequently integrates genotype and tissue expression data together with predicted cell proportions for samples in BIOS to detect cell type eQTLs. We validated Decon-eQTL using multiple independent sources, including expression profiles of purified cell subpopulations, eQTLs and chromatin mark QTLs (cmQTLs) from purified neutrophils, monocytes CD14+ and CD4+ T cells , and single cell eQTLs results . Benchmarking of Decon-eQTL was carried out for comparison with previously reported methods which detected cell type eQTL effects using whole blood expression data, i.e. Westra method and Zhernakova, et al method ).

Article Snippet: We have validated our Decon-eQTL results by using eQTLs from purified neutrophils, CD14+ monocytes and CD4+ T-cells.

Techniques: Expressing, Marker, RNA Sequencing, Purification, Comparison

Journal: bioRxiv

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1101/548669

Figure Lengend Snippet: (A) Distribution of prediction performance (Spearman correlation coefficient) of the 34 predictable cell types in 100 iterations of prediction within the 500FG cohort. (B) Cross-cohort validation in an independent Lifelines-Deep cohort (n=627): the measured and predicted cell proportions for neutrophils (given by granulocytes in 500FG), lymphocytes and monocytes are compared.

Article Snippet: We have validated our Decon-eQTL results by using eQTLs from purified neutrophils, CD14+ monocytes and CD4+ T-cells.

Techniques: Biomarker Discovery

Journal: bioRxiv

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1101/548669

Figure Lengend Snippet: (A) Expression of eQTL genes in purified cell subpopulations from BLUEPRINT is significantly higher in its relevant cell subpopulation compared to other available cell subtypes (green for granulocyte eQTL genes showing expression for purified neutrophils; orange for monocytes; purple for CD4+ T cells; pink for B cells). (B) Differential expressed genes (Adjusted p-value ≤ 0.5) between CD4+ T cells and NK cells are significantly enriched for CT eQTLs effects on CD4+ T cells (dots in purple, Fisher exact P = 1.8×10 17 ) and NK Cells (dots in yellow, Fisher exact P = 2.3×10 18 ) respectively. (C) Deconvoluted eQTLs (FDR ≤ 0.05) show significantly larger effect sizes in the purified cell eQTLs data compared to the rest of the whole blood eQTLs for which we do not detect cell type effect, as shown for deconvoluted granulocyte eQTLs in neutrophil derived eQTLs (green); monocytes (orange); CD4+ T cells (purple).

Article Snippet: We have validated our Decon-eQTL results by using eQTLs from purified neutrophils, CD14+ monocytes and CD4+ T-cells.

Techniques: Expressing, Purification, Derivative Assay

Journal: bioRxiv

Article Title: Deconvolution of bulk blood eQTL effects into immune cell subpopulations

doi: 10.1101/548669

Figure Lengend Snippet: Deconvoluted CT QTLs show high allelic concordance compared to eQTLs from purified cell subpopulations . (A) for granulocyte eQTLs (orange), Decon-eQTL achieved an allelic concordance of 99% compared to eQTLs from purified neutrophils. Similarly, the allelic concordance were 96%and 99% for monocytes and CD4+ T cells, respectively. They are higher than those observed for whole blood eQTLs when comparing to eQTLs from purified subpopulations as shown in panel (B). Deconvoluted eQTLs show an allelic concordance of 95% for significant eQTLs obtained from single cell RNA-seq data on monocytes CD14+, B cells, CD4+ T cells, CD8+ T cells and NK cells (C).

Article Snippet: We have validated our Decon-eQTL results by using eQTLs from purified neutrophils, CD14+ monocytes and CD4+ T-cells.

Techniques: Purification, RNA Sequencing