In vincent-van-hoef/Analysis5204: Analysis for NBIS support project #5204

Cortisone

Protein-level

Active disease patients were either treated with cortisone or not treated with cortisone. Differential analysis between these both groups and for each phenotype was calculated as in Section \@ref(univariate-analysis); i.e. per-protein anova on age and ckd_epi corrected data followed by a post-hoc analysis to determine the significant comparisons.

Results for the per-protein analysis is collected per phenotype in Results/Plasma/Cortisone/phenotype and Results/Serum/Cortisone/phenotype.

The volcano plot for cortisone vs no_cortisone in GPA AAV is shown in Figure \@ref(fig:cortplots) (Left).

knitr::include_graphics(c("Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_volcano_anova_posthoc.pdf"))

The top 15 of most significantly regulated proteins (ranked by adjusted p-value) of cortisone versus no_cortisone in GPA AAV is shown in \@ref(fig:top15Cort). The "estimate" column can be considered as a log2 fold change (i.e. a difference of 1 indicates a doubled expression in cortisone versus no cortisone).

knitr::include_graphics(c("Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_Top15_table.png"))

GSEA

GSEA was performed for cortisone versus no_cortisone for the different phenotypes as in Section \@ref(gsea).

As before, the GSEA results folder in the Cortisone folder contains a .xlsx file containing the full GSEA results and two different visualizations of the top terms (see Figure \@ref(fig:gseaPlotsCort)). These figures include a barplot of the gene sets with the 6 highest and 6 lowest NES values and an Enrichment map of the 20 gene sets with the highest absolute NES values. Enrichment maps organize gene sets into a network with edges connecting overlapping gene sets. In this way, mutually overlapping gene sets tend to cluster together, making it easier to identify functional modules.

knitr::include_graphics(c("Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_barplot.png", "Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_emap.png"))

In addition, in the Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/GSEA_PLOTS and Results/Serum/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/GSEA_PLOTS folder you can find a visualization of the location of the gene set genes in the ranked gene list and ES calculation visualization (see Figure \@ref(fig:gseaNesCort)).

knitr::include_graphics(c("Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/GSEA_PLOTS/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_gseaplot_POSITIVE_REGULA.png", "Results/Plasma/Cortisone/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone/Univariate/GSEA/GSEA_PLOTS/active_disease_GPA_Cortisone_vs_active_disease_GPA_No_Cortisone_gseaplot_PROTEOLYSIS.png"))

vincent-van-hoef/Analysis5204 documentation built on June 24, 2022, 2:41 p.m.