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Fkbp5_DNAm_HAMTBS_humanized_mouse_humans/analysis_script.R
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| # Analysis script for the paper: | |
| # "DNA methylation patterns of FKBP5 regulatory regions in brain and blood of | |
| # humanized mice and humans" | |
| # ============================================================================== | |
| # Author: Natan Yusupov, natan_yusupov@psych.mpg.de | |
| # Description: | |
| # The script analyzes DNAm data in three functional intronic glucocorticoid-responsive | |
| # elements (introns 2, 5 and 7) quantified by targeted bisulfite sequencing (HAM-TBS) | |
| # in three tissues/brain regions (blood, prefrontal cortex and hippocampus) of mice | |
| # carrying the human FK506-binding protein 5 (FKBP5) gene of FKBP5 at baseline, | |
| # in cases of differing genotype (rs1360780 single nucleotide polymorphism), and | |
| # following application of dexamethasone. Further analyses compare DNAm patterns | |
| # in the humanized mouse to those in human peripheral blood and human postmortem | |
| # brain prefrontal cortex. | |
| # Summary of steps performed in the script "analysis_script.R": | |
| # 1. Load libraries and data | |
| # 2. Prepare data for analysis | |
| # 3. Mean and SD in humanized mouse tissues (Supplementary Table 5) | |
| # 4. Spearman correlation coefficients between CpGs of humanized mouse tissue | |
| # (Supplementary Table 7 and Supplementary Figure 2) | |
| # 5. Comparison DNAm in human blood at baseline study 1 and 2 (Supplementary Table 8) | |
| # 6. Mean and SD in human and humanized mouse blood (Supplementary Table 9) | |
| # 7. Delta mean of DNAm in blood between humanized mouse and humans (Supplementary Table 10) | |
| # 8. Mean and SD in human and humanized mouse PFC (Supplementary Table 11) | |
| # 9. Delta mean of DNAm in PFC between humanized mouse and humans (Supplementary Table 12) | |
| # 10. Delta mean of DNAm in PFC and blood between humanized mouse and humans | |
| # aged 20-29 years (Supplementary Table 13) | |
| # 11. Multivariate linear regressions in humanized mouse (Supplementary Tables | |
| # 14, 15, 16, and parts of Supplementary Figures 5, 8, 9) | |
| # A. Blood time point 0 | |
| # B. Blood time point 4 | |
| # C. Blood time point 24 hours | |
| # D. PFC time point 0 | |
| # E. PFC time point 4 hours | |
| # F. PFC time point 24 hours | |
| # G. HIP time point 0 | |
| # H. HIP time point 4 hours | |
| # I. HIP time point 24 hours | |
| # 12. Interindividual variability of DNAm in humanized mouse tissues (Supplementary Figure 1) | |
| # 13. Correlation matrix of CpGs in the humanized mouse tissues (Supplementary Figure 3) | |
| # 14. Genotype effects on DNAm in humanized mouse and human blood (Supplementary Figure 5) | |
| # 15. Genotype effects on DNAm in humanized mouse and human PFC (Supplementary Figure 6) | |
| # 16. Dexamethasone effects on DNAm in humanized mouse and human blood (Supplementary Figure 7) | |
| # 17. Nominal interactions effects of dexamethasone with rs1360780 on DNAm | |
| # in humanized mouse hippocampus (Supplementary Figure 9) | |
| # 18. DNAm patterns in different tissues of humanized mouse (Figure 2) | |
| # 19. DNAm patterns at baseline in blood and PFC of humanized mouse and humans (Figure 3) | |
| # 20. Age dependent DNAm patterns in blood and PFC between humanized mouse and human (Figure 4) | |
| # 21. Dexamethasone effects on DNAm per CpG in humanized mouse and human blood (Figure 5) | |
| # 22. Session information | |
| # ============================================================================== | |
| # 1. Load libraries and data | |
| library(tidyverse) | |
| library(readxl) | |
| library(writexl) | |
| library(broom) | |
| library(ggpubr) | |
| library(Hmisc) | |
| library(trio) | |
| library(corrplot) | |
| library(janitor) | |
| library(reshape) | |
| intron_2 <- readRDS("data/cpg_locations_intron2_vector.RDS") | |
| intron_5 <- readRDS("data/cpg_locations_intron5_vector.RDS") | |
| intron_7 <- readRDS("data/cpg_locations_intron7_vector.RDS") | |
| cpg_locations <- readRDS("data/cpg_locations_vector.RDS") | |
| path <- "/Users/natan_yusupov/Desktop/github_repos/Fkbp5_DNAm_HAMTBS_humanized_mouse_humans/" | |
| human_blood_study1_df <- read.csv(paste0(path, "data/human_blood_study1_df.csv"), | |
| sep = ",", row.names = 1, check.names = FALSE) | |
| human_blood_study1_df <- human_blood_study1_df %>% | |
| mutate(sex = as.factor(sex), rs1360780_T = as.factor(rs1360780_T), agebin = as.factor(agebin), | |
| sample = paste0(sample, "_1")) | |
| human_blood_study2_df <- read.csv(paste0(path, "data/human_blood_study2_df.csv"), | |
| sep = ",", row.names = 1, check.names = FALSE) | |
| human_blood_study2_df <- human_blood_study2_df %>% | |
| mutate(Dex__0_baseline__1_3h__2_24h_ = as.factor(Dex__0_baseline__1_3h__2_24h_), | |
| rs1360780_T = as.factor(rs1360780_T), sex = as.factor(sex), | |
| dex_dichot = as.factor(dex_dichot), | |
| sample = paste0(sample, "_2")) | |
| human_postmortem_brain_study3_df <- read.csv(paste0(path, "data/human_postmortem_brain_study3_df.csv"), | |
| sep = ",", row.names = 1, check.names = FALSE) | |
| human_postmortem_brain_study3_df <- human_postmortem_brain_study3_df %>% | |
| mutate(agebin = as.factor(agebin), sex = as.factor(sex), | |
| rs1360780_T = as.factor(rs1360780_T), rs1360780_anyT = as.factor(rs1360780_anyT), | |
| sample = paste0(sample, "_3")) | |
| humanized_mouse_df <- read.csv(paste0(path, "data/humanized_mouse_df.csv"), | |
| sep = ",", row.names = 1, check.names = FALSE) | |
| humanized_mouse_df <- humanized_mouse_df %>% | |
| mutate(Genotype = as.factor(Genotype), Timegroup = as.factor(Timegroup), | |
| Group = as.factor(Group), Dissecter = as.factor(Dissecter), Tissue = as.factor(Tissue)) | |
| results_wiechmann_df <- read.csv(paste0(path, "data/results_wiechmann_df.csv"), | |
| sep = ",", row.names = 1, check.names = FALSE) | |
| # 2. Prepare data for analysis | |
| methylation_human_blood_study1 <- human_blood_study1_df %>% | |
| column_to_rownames(var = "sample") %>% | |
| dplyr::select(contains(cpg_locations)) | |
| methylation_human_blood_study2_baseline <- human_blood_study2_df %>% | |
| filter(dex_dichot == "0") # only baseline | |
| methylation_humouse_blood <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with("_corrected")) %>% | |
| filter(Tissue == "Blood", Group %in% c("No Treatment", "V")) %>% # only untreated | |
| dplyr::select(sample, contains(cpg_locations)) %>% | |
| column_to_rownames("sample") %>% | |
| mutate(study = "Humanized Mouse") | |
| covariates_humouse <- humanized_mouse_df %>% | |
| dplyr::select(sample, Group, Genotype, Tissue) | |
| methylation_blood_combined <- rbind(methylation_human_blood_study1, methylation_humouse_blood) | |
| methylation_blood_combined_long <- methylation_blood_combined %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| methylation_human_postmortem_brain <- human_postmortem_brain_study3_df %>% | |
| dplyr::select(sample, ends_with("_corrected")) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, contains(cpg_locations)) %>% | |
| mutate(study = "Human") %>% | |
| column_to_rownames("sample") | |
| methylation_humouse_pfc <- humanized_mouse_df %>% | |
| filter(Tissue == "PFC", Group %in% c("No Treatment", "V")) %>% # only untreated | |
| dplyr::select(sample, ends_with("_corrected")) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, contains(cpg_locations)) %>% | |
| column_to_rownames("sample") %>% | |
| mutate(study = "Humanized Mouse") | |
| methylation_pfc_combined <- rbind(methylation_human_postmortem_brain, methylation_humouse_pfc) | |
| methylation_pfc_combined_long <- methylation_pfc_combined %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| # 3. Mean and SD in humanized mouse tissues (Supplementary Table 5) | |
| methylation_humouse_all_tissues <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| filter(Group %in% c("No Treatment", "V")) %>% # only untreated | |
| dplyr::select(sample, Tissue, contains(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) | |
| methylation_humouse_all_tissues_long <- methylation_humouse_all_tissues %>% | |
| pivot_longer(cols = -c(sample, Tissue), names_to = "CpG", values_to = "methylation") | |
| methylation_humouse_all_tissues_mean_sd <- methylation_humouse_all_tissues %>% | |
| group_by(Tissue) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -Tissue, names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) | |
| # 4. Spearman correlation coefficients between CpGs of humanized mouse tissue | |
| # (Supplementary Table 7 and Supplementary Figure 2) | |
| humouse_tissue_cpg_level <- methylation_humouse_all_tissues %>% | |
| mutate(sample = str_sub(sample, 1, 6)) %>% | |
| filter(Tissue == "Blood") %>% | |
| dplyr::select(-Tissue) %>% | |
| rename_at(.vars = vars(`35558386`:`35608022`), .funs = ~ paste0(., "_Blood")) %>% | |
| left_join(methylation_humouse_all_tissues %>% mutate(sample = str_sub(sample, 1, 6)) %>% | |
| filter(Tissue == "PFC") %>% dplyr::select(-Tissue) %>% | |
| rename_at(.vars = vars(`35558386`:`35608022`), .funs = ~ paste0(., "_PFC"))) %>% | |
| left_join(methylation_humouse_all_tissues %>% mutate(sample = str_sub(sample, 1, 6)) %>% | |
| filter(Tissue == "HIP") %>% dplyr::select(-Tissue) %>% | |
| rename_at(.vars = vars(`35558386`:`35608022`), .funs = ~ paste0(., "_HIP"))) | |
| corr_brain_blood_humouse <- humouse_tissue_cpg_level %>% | |
| tibble::remove_rownames() %>% | |
| column_to_rownames("sample") %>% | |
| as.matrix() %>% | |
| rcorr(type = "spearman") | |
| corr_matrix_brain_blood_humouse <- corr_brain_blood_humouse$r | |
| corr_matrix_brain_blood_humouse_df <- corr_matrix_brain_blood_humouse %>% | |
| as.data.frame() %>% | |
| rownames_to_column("CpG") | |
| corr_brain_blood_humouse_pvalue <- corr_brain_blood_humouse$P | |
| plot_corr_brain_blood_humouse <- corrplot(corr_brain_blood_humouse$r, method = "color", | |
| p.mat = corr_brain_blood_humouse_pvalue, | |
| insig = "pch", pch = "ns", sig.level = 0.05, | |
| tl.cex = 0.4, pch.cex = 0.3, tl.col = "darkblue", | |
| tl.srt = 45, mar=c(0,0,2,0)) | |
| cor(humouse_tissue_cpg_level$`35570224_Blood`, humouse_tissue_cpg_level$`35570224_PFC`, use="complete.obs", method = "spearman") | |
| cor(humouse_tissue_cpg_level$`35570224_Blood`, humouse_tissue_cpg_level$`35570224_HIP`, use="complete.obs", method = "spearman") | |
| cor(humouse_tissue_cpg_level$`35570224_PFC`, humouse_tissue_cpg_level$`35570224_HIP`, use="complete.obs", method = "spearman") | |
| # 5. Comparison DNAm in human blood at baseline study 1 and 2 (Supplementary Table 8) | |
| outcome_variables <- methylation_human_blood_study1 %>% | |
| dplyr::select(starts_with("35")) %>% | |
| colnames() | |
| human_blood_study1_df_subset <- human_blood_study1_df %>% | |
| dplyr::select(-rs1360780_T, -agebin) %>% | |
| na.omit() | |
| regressors <- c("age", "sex", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran") | |
| lm_tidy <- function() { | |
| results <- list() | |
| for (i in outcome_variables) { | |
| f <- as.formula(paste0("`", i, "`", "~", paste(regressors, collapse = "+"))) | |
| tidy <- augment(lm(formula = f, data = human_blood_study1_df_subset, na.action = na.omit)) | |
| tidy <- tidy %>% dplyr::select(.resid) | |
| colnames(tidy) <- as.character(i) | |
| results[[i]] <- tidy | |
| } | |
| results | |
| } | |
| reisduals <- lm_tidy() | |
| methylation_human_blood_study1_regressed_out <- as.data.frame(do.call(cbind, reisduals)) | |
| rownames(methylation_human_blood_study1_regressed_out) <- human_blood_study1_df_subset$sample | |
| methylation_human_blood_study1_regressed_out <- methylation_human_blood_study1_regressed_out %>% | |
| mutate(study = as.factor("Human Study 1")) | |
| outcome_variables <- methylation_human_blood_study2_baseline %>% | |
| dplyr::select(starts_with("35")) %>% | |
| colnames() | |
| regressors <- c("age", "sex", "Neutrophil", "Monocyte", "Bcell", "NKcell", "Tcell") | |
| lm_tidy <- function() { | |
| results <- list() | |
| for (i in outcome_variables) { | |
| f <- as.formula(paste0("`", i, "`", "~", paste(regressors, collapse = "+"))) | |
| tidy <- augment(lm(formula = f, data = methylation_human_blood_study2_baseline, na.action = na.omit)) | |
| tidy <- tidy %>% dplyr::select(.resid) | |
| colnames(tidy) <- as.character(i) | |
| results[[i]] <- tidy | |
| } | |
| results | |
| } | |
| reisduals <- lm_tidy() | |
| methylation_human_blood_study2_regressed_out <- as.data.frame(do.call(cbind, reisduals)) | |
| rownames(methylation_human_blood_study2_regressed_out) <- methylation_human_blood_study2_baseline$sample | |
| methylation_human_blood_study2_regressed_out <- methylation_human_blood_study2_regressed_out %>% | |
| mutate(study = as.factor("Human Study 2")) | |
| methylation_human_blood_study1 <- methylation_human_blood_study1 %>% | |
| mutate(study = as.factor("Human Study 1")) | |
| methylation_human_blood_study2_baseline <- methylation_human_blood_study2_baseline %>% | |
| mutate(study = as.factor("Human Study 2")) | |
| methylation_human_blood_2_studies <- methylation_human_blood_study2_baseline %>% | |
| dplyr::select(sample, starts_with("35"), study) %>% | |
| bind_rows(methylation_human_blood_study1 %>% rownames_to_column("sample")) %>% | |
| relocate(study, .after = last_col()) | |
| methylation_human_blood_2_studies_long <- methylation_human_blood_2_studies %>% | |
| dplyr::select(!starts_with("3560")) %>% # remove intron 2 since missing in study 2 | |
| pivot_longer(cols = -c(sample, study), names_to = "CpG", values_to = "methylation") | |
| methylation_human_blood_2_studies_regressed_out <- methylation_human_blood_study2_regressed_out %>% | |
| bind_rows(methylation_human_blood_study1_regressed_out) %>% | |
| relocate(study, .after = last_col()) | |
| methylation_human_blood_2_studies_regressed_out_long <- methylation_human_blood_2_studies_regressed_out %>% | |
| rownames_to_column("sample") %>% | |
| dplyr::select(!starts_with("3560")) %>% # remove intron 2 since missing in study 2 | |
| pivot_longer(cols = -c(sample, study), names_to = "CpG", values_to = "methylation") | |
| t_test_human_blood_studies <- compare_means(methylation ~ study, | |
| data = methylation_human_blood_2_studies_long, | |
| group.by = "CpG", method = "t.test") %>% | |
| mutate(p.adj = p*16, | |
| p.adj.signif = case_when(p < 0.05/16 & p >= 0.01/16 ~ "*", | |
| p < 0.01/16 & p >= 0.001/16 ~ "**", | |
| p < 0.001/16 & p >= 0.0001/16 ~ "***", | |
| p < 0.0001/16 ~ "****", | |
| p > 0.05/16 ~ "ns", | |
| TRUE ~ "error")) | |
| t_test_human_blood_studies_regressed_out <- compare_means(methylation ~ study, | |
| data = methylation_human_blood_2_studies_regressed_out_long, | |
| group.by = "CpG", method = "t.test") | |
| # 6. Mean and SD in human and humanized mouse blood (Supplementary Table 9) | |
| methylation_human_humouse_blood_mean_sd <- methylation_blood_combined %>% | |
| group_by(study) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -study, names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) | |
| methylationn_human_blood_study2_mean_sd <- results_wiechmann_df %>% | |
| filter(CpG %in% cpg_locations) %>% | |
| dplyr::select(CpG, mean = "mean_baseline", sd = "sd_baseline") %>% | |
| mutate(study = "Human Study 2") | |
| methylation_blood_baseline_three_sets_df <- rbind(methylationn_human_blood_study2_mean_sd, | |
| methylation_human_humouse_blood_mean_sd) | |
| # 7. Delta mean of DNAm in blood between humanized mouse and humans (Supplementary Table 10) | |
| delta_baseline_blood_three_sets_df <- methylation_blood_baseline_three_sets_df %>% | |
| dplyr::select(-sd) %>% | |
| pivot_wider(names_from = study, values_from = mean) %>% | |
| dplyr::select(CpG, `Humanized Mouse`, `Human Study 1`, `Human Study 2`) %>% | |
| mutate(`Delta DNAm Human Study 1` = `Humanized Mouse` - `Human Study 1`, | |
| `Delta DNAm Human Study 2` = `Humanized Mouse` - `Human Study 2`) | |
| # 8. Mean and SD in human and humanized mouse PFC (Supplementary Table 11) | |
| methylation_human_postmortem_humouse_mean_sd <- methylation_pfc_combined %>% | |
| group_by(study) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -study, names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) | |
| # 9. Delta mean of DNAm in PFC between humanized mouse and humans (Supplementary Table 12) | |
| delta_baseline_pfc_human_humouse_df <- methylation_human_postmortem_humouse_mean_sd %>% | |
| dplyr::select(-sd) %>% | |
| pivot_wider(names_from = study, values_from = mean) %>% | |
| dplyr::select(CpG, `Humanized Mouse`, Human) %>% | |
| mutate(`Delta DNAm` = `Humanized Mouse` - Human) | |
| # 10. Delta mean of DNAm in PFC and blood between humanized mouse and humans | |
| # aged 20-29 years (Supplementary Table 13) | |
| methylation_human_blood_agebins <- methylation_human_blood_study1 %>% | |
| rownames_to_column("sample") %>% | |
| left_join(human_blood_study1_df %>% dplyr::select(sample, agebin)) %>% | |
| mutate(study = "Human") | |
| methylation_blood_combined_agebins <- bind_rows(methylation_human_blood_agebins, | |
| methylation_humouse_blood %>% rownames_to_column("sample")) | |
| methylation_blood_combined_agebins_long <- methylation_blood_combined_agebins %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| methylation_human_humouse_blood_mean_sd_agebins <- methylation_blood_combined_agebins %>% | |
| group_by(study, agebin) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -c(study, agebin), names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) %>% | |
| mutate(agebin = case_when(!is.na(agebin) ~ paste0("Human ", agebin), | |
| TRUE ~ "Humanized Mouse")) | |
| methylation_human_postmortem_brain_agebins <- methylation_human_postmortem_brain %>% | |
| rownames_to_column("sample") %>% | |
| left_join(human_postmortem_brain_study3_df %>% dplyr::select(sample, agebin), by = "sample") | |
| methylation_pfc_combined_agebins <- bind_rows(methylation_human_postmortem_brain_agebins, | |
| methylation_humouse_pfc %>% rownames_to_column("sample")) | |
| methylation_pfc_combined_agebins_long <- methylation_pfc_combined_agebins %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| methylation_human_humouse_pfc_mean_sd_agebins <- methylation_pfc_combined_agebins %>% | |
| group_by(study, agebin) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -c(study, agebin), names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) %>% | |
| mutate(agebin = case_when(!is.na(agebin) ~ paste0("Human ", agebin), | |
| TRUE ~ "Humanized Mouse")) | |
| delta_baseline_blood_human_humouse_agebins_df <- methylation_human_humouse_blood_mean_sd_agebins %>% | |
| ungroup() %>% | |
| dplyr::select(-c(sd, study)) %>% | |
| filter(agebin %in% c("Human 20-29", "Humanized Mouse")) %>% | |
| pivot_wider(names_from = agebin, values_from = mean) %>% | |
| mutate(`Delta DNAm` = `Humanized Mouse` - `Human 20-29`) | |
| delta_baseline_pfc_human_humouse_agebins_df <- methylation_human_humouse_pfc_mean_sd_agebins %>% | |
| ungroup() %>% | |
| dplyr::select(-c(sd, study)) %>% | |
| filter(agebin %in% c("Human 20-29", "Humanized Mouse")) %>% | |
| pivot_wider(names_from = agebin, values_from = mean) %>% | |
| mutate(`Delta DNAm` = `Humanized Mouse` - `Human 20-29`) | |
| # 11. Multivariate linear regressions in humanized mouse (Supplementary Tables 14, 15, 16, | |
| # and parts of Supplementary Figures 5, 8, 9) | |
| cpgs_remain_blood <- iqr_cpg_tissue %>% # filter out CpGs with IQR<1 | |
| filter(Tissue == "Blood") %>% | |
| filter(IQR > 1) %>% | |
| pull(CpG) | |
| cpgs_remain_pfc <- iqr_cpg_tissue %>% | |
| filter(Tissue == "PFC") %>% | |
| filter(IQR > 1) %>% | |
| pull(CpG) | |
| cpgs_remain_hip <- iqr_cpg_tissue %>% | |
| filter(Tissue == "HIP") %>% | |
| filter(IQR > 1) %>% | |
| pull(CpG) | |
| covariates_mlr_humouse <- humanized_mouse_df %>% | |
| dplyr::select(sample, Genotype, Timegroup, Group, Dissecter, column, Tissue) | |
| methylation_humouse_mval <- humanized_mouse_df %>% | |
| dplyr::select(sample, Tissue, ends_with("_mval")) %>% | |
| rename_with(~str_remove(., "_mval")) | |
| reduced_df_wide_blood <- methylation_humouse_mval %>% | |
| filter(Tissue == "Blood") %>% | |
| dplyr::select(sample, all_of(cpgs_remain_blood)) %>% | |
| left_join(covariates_mlr_humouse, by = "sample") | |
| reduced_df_wide_pfc <- methylation_humouse_mval %>% | |
| filter(Tissue == "PFC") %>% | |
| dplyr::select(sample, all_of(cpgs_remain_pfc)) %>% | |
| left_join(covariates_mlr_humouse, by = "sample") | |
| reduced_df_wide_hip <- methylation_humouse_mval %>% | |
| filter(Tissue == "HIP") %>% | |
| dplyr::select(sample, all_of(cpgs_remain_hip)) %>% | |
| left_join(covariates_mlr_humouse, by = "sample") | |
| reduced_df_mval_all_tissues <- dplyr::bind_rows(reduced_df_wide_blood, reduced_df_wide_pfc, reduced_df_wide_hip) | |
| reduced_df_mval_all_tissues <- reduced_df_mval_all_tissues %>% | |
| mutate(Group = fct_relevel(Group, c("No Treatment", "V", "DEX")), | |
| Timegroup = fct_relevel(Timegroup, c("t0", "t4", "t24")), | |
| Tissue = fct_relevel(Tissue, c("Blood", "PFC", "HIP"))) | |
| # A. Blood time point 0 | |
| outcome <- reduced_df_wide_blood %>% | |
| dplyr::select(starts_with("35")) %>% | |
| colnames() %>% sort() | |
| predictor <- "Genotype" | |
| lm_summary <- function(tissue, timegroup){ # function for MLR in different data subsets | |
| data_sub <- reduced_df_mval_all_tissues %>% filter(Tissue == tissue, Timegroup %in% timegroup) | |
| results <- list() | |
| for(i in outcome){ | |
| f <- as.formula(paste0("`", i, "`", "~", paste(predictor, collapse = "*"))) | |
| tidy <- tidy(lm(formula = f, data = data_sub, na.action = na.omit)) | |
| results[[i]] <- tidy | |
| } | |
| results | |
| } | |
| blood_0 <- lm_summary("Blood", "t0") | |
| blood_0_df <- purrr::map_dfr(blood_0, ~ .x, .id = "CpG") %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_blood_0 <- blood_0_df %>% | |
| filter(term != "(Intercept)") %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = p.value < 0.05, | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1, "*", "")))) + | |
| geom_col(width = 0.2) + | |
| theme_bw() + | |
| geom_text(vjust = 0.5, hjust = -1, size = 3) + | |
| scale_fill_manual(values= c("grey", "black")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "Genotype Effect in Blood") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # B. Blood time point 4 | |
| predictor <- c("Genotype", "Group") | |
| blood_4 <- lm_summary("Blood", "t4") | |
| blood_4_df <- purrr::map_dfr(blood_4, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_blood_4 <- blood_4_df %>% | |
| filter(!term %in% c("GenotypeRiA", "(Intercept)")) %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = ifelse(p.value < 0.05, 1, 0.2), | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = -1, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse(limit = c(0,-1)) + | |
| labs(x = "Effect Size", y = "CpG", title = "Dexamethasone Effect in Blood (4 Hours)") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # C. Blood time point 24 hours | |
| blood_24 <- lm_summary("Blood", "t24") | |
| blood_24_df <- purrr::map_dfr(blood_24, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_blood_24 <- blood_24_df %>% | |
| filter(!term %in% c("GenotypeRiA", "(Intercept)")) %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = ifelse(p.value < 0.05, 1, 0.2), | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = -1, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "After 24h - Blood") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # D. PFC time point 0 | |
| outcome <- reduced_df_wide_pfc %>% | |
| dplyr::select(starts_with("35")) %>% | |
| colnames() %>% sort() | |
| predictor <- "Genotype" | |
| covariates <- c("Dissecter", "column") | |
| lm_summary <- function(tissue, timegroup){ | |
| data_sub <- final_df_humouse %>% filter(Tissue == tissue, Timegroup == timegroup) | |
| results <- list() | |
| for(i in outcome){ | |
| f <- as.formula(paste0("`", i, "`", "~", paste(covariates, collapse = "+"), "+", | |
| paste(predictor, collapse = "*"))) | |
| tidy <- tidy(lm(formula = f, data = data_sub, na.action = na.omit)) | |
| results[[i]] <- tidy | |
| } | |
| results | |
| } | |
| pfc_0 <- lm_summary("PFC", "t0") | |
| pfc_0_df <- purrr::map_dfr(pfc_0, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_pfc_0 <- pfc_0_df %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = p.value < 0.05, | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(values= c("grey", "black")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "Genotype Effect in PFC") + | |
| theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 8), | |
| axis.text.x = element_text(size = 8, hjust = 1, angle = 90), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| annotate("rect", ymin = -Inf, ymax = "35569751", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35578891", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # E. PFC time point 4 hours | |
| predictor <- c("Genotype", "Group") | |
| pfc_4 <- lm_summary("PFC", "t4") | |
| pfc_4_df <- purrr::map_dfr(pfc_4, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_pfc_4 <- pfc_4_df %>% | |
| filter(term != "GenotypeRiA") %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = ifelse(p.value < 0.05, 1, 0.2), | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "After 4h - PFC") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # F. PFC time point 24 hours | |
| pfc_24 <- lm_summary("PFC", "t24") | |
| pfc_24_df <- purrr::map_dfr(pfc_24, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_pfc_24 <- pfc_24_df %>% | |
| filter(term != "GenotypeRiA") %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = ifelse(p.value < 0.05, 1, 0.2), | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "After 24h - PFC") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| pfc_0_df <- purrr::map_dfr(pfc_0, ~ .x, .id = "CpG") %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| pfc_4_df <- purrr::map_dfr(pfc_4, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| pfc_24_df <- purrr::map_dfr(pfc_24, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| # G. HIP time point 0 | |
| outcome <- reduced_df_wide_hip %>% | |
| dplyr::select(starts_with("35")) %>% | |
| colnames() %>% sort() | |
| predictor <- "Genotype" | |
| hip_0 <- lm_summary("HIP", "t0") | |
| hip_0_df <- purrr::map_dfr(hip_0, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_hip_0 <- hip_0_df %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = p.value < 0.05, | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(values= c("grey", "black")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "Genotype Effect in HIP") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # H. HIP time point 4 hours | |
| predictor <- c("Genotype", "Group") | |
| hip_4 <- lm_summary("HIP", "t4") | |
| hip_4_df <- purrr::map_dfr(hip_4, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_hip_4 <- hip_4_df %>% | |
| filter(term != "GenotypeRiA") %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = p.value < 0.05, | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "Dexamethasone Effect in HIP (4 Hours)") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| # I. HIP time point 24 hours | |
| hip_24 <- lm_summary("HIP", "t24") | |
| hip_24_df <- purrr::map_dfr(hip_24, ~ .x, .id = "CpG") %>% | |
| filter(!term %in% c("(Intercept)", "DissecterLi"), !str_detect(term, "column")) %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| plot_hip_24 <- hip_24_df %>% | |
| filter(term != "GenotypeRiA") %>% | |
| ggplot(aes(x = estimate, y = CpG, fill = term, alpha = p.value < 0.05, | |
| label = ifelse(q.value < 0.05, "**", ifelse(q.value < 0.1 & p.value < 0.05, "*", "")))) + | |
| geom_col(position = "dodge", width = 0.4) + | |
| geom_text(vjust = 0.5, hjust = 1.5, size = 3) + | |
| theme_bw() + | |
| scale_fill_manual(labels = c("GenotypeRiA:DEX", "DEX"), | |
| values = c("goldenrod1", "darkgreen")) + | |
| scale_x_reverse() + | |
| labs(x = "Effect Size", y = "CpG", title = "Dexamethasone Effect in HIP (24 Hours)") + | |
| theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12, hjust = 1, angle = 90), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| guides(color=guide_legend("alpha"), alpha = FALSE) + | |
| annotate("rect", ymin = -Inf, ymax = "35558721", xmin = -Inf, xmax = Inf, fill = "lightgreen", alpha = 0.2) + | |
| annotate("rect", ymin = "35569751", ymax = "35578891", xmin = -Inf, xmax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", ymin = "35607856", ymax = Inf, xmin = -Inf, xmax = Inf, fill = "red4", alpha = 0.2) | |
| hip_0_df <- purrr::map_dfr(hip_0, ~ .x, .id = "CpG") %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| hip_4_df <- purrr::map_dfr(hip_4, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| hip_24_df <- purrr::map_dfr(hip_24, ~ .x, .id = "CpG") %>% | |
| group_by(term) %>% | |
| mutate(q.value = p.adjust(p.value, method = "fdr")) | |
| # 12. Interindividual variability of DNAm in humanized mouse tissues (Supplementary Figure 1) | |
| iqr_cpg_tissue <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, Tissue, contains(cpg_locations)) %>% | |
| group_by(Tissue) %>% | |
| summarise(across(-c(sample), IQR, na.rm = TRUE)) %>% | |
| pivot_longer(cols = -Tissue, names_to = "CpG", values_to = "IQR") %>% | |
| arrange(desc(IQR)) %>% | |
| mutate(functional_region = as.factor(case_when( | |
| CpG %in% intron_7 ~ "Intron 7", | |
| CpG %in% intron_5 ~ "Intron 5", | |
| CpG %in% intron_2 ~ "Intron 2", | |
| TRUE ~ "error"))) | |
| mean_iqr_cpg_tissue <- iqr_cpg_tissue %>% | |
| group_by(Tissue, functional_region) %>% | |
| summarise(Mean_IQR = mean(IQR)) | |
| iqr_cpg_cutoff <- iqr_cpg_tissue %>% | |
| mutate(IQR = ifelse(CpG == "35607969" & Tissue == "Blood", 0.4, IQR)) %>% | |
| # change for visualization, otherwise rounds up and gets into wrong bin by ggplot | |
| ggplot(aes(x = IQR)) + | |
| geom_histogram(boundary=0.997, bins = 30) + | |
| geom_vline(color = "darkred", xintercept = 1) + | |
| theme_bw() + | |
| scale_x_continuous(breaks = seq(0, 21, by = 1)) + | |
| theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 10), | |
| axis.text.y = element_text(size = 10, hjust = 1), | |
| axis.text.x = element_text(size = 10, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| facet_wrap(~Tissue, nrow = 3) | |
| iqr_cpg_by_region_blood <- iqr_cpg_tissue %>% | |
| filter(Tissue == "Blood") %>% | |
| ggplot(aes(x = reorder(CpG, -IQR), y = IQR, fill = functional_region)) + | |
| geom_col() + | |
| geom_hline(color = "darkred", yintercept = 1) + | |
| labs(y = "IQR", x = "CpG", title = "IQR of DNAm Levels Across CpGs - Blood") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 10), | |
| axis.text.x = element_text(size = 8, hjust = 1, angle = 45), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| scale_y_continuous(breaks = seq(0, 21, by = 1), limits = c(0,21)) + | |
| scale_fill_manual("legend", values = alpha(c("Intron 7" = "lightgreen", | |
| "Intron 5" = "royalblue4", | |
| "Intron 2" = "red4"), 0.6)) | |
| iqr_cpg_by_region_pfc <- iqr_cpg_tissue %>% | |
| filter(Tissue == "PFC") %>% | |
| ggplot(aes(x = reorder(CpG, -IQR), y = IQR, fill = functional_region)) + | |
| geom_col() + | |
| geom_hline(color = "darkred", yintercept = 1) + | |
| labs(y = "IQR", x = "CpG", title = "IQR of DNAm Levels Across CpGs - PFC") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 10), | |
| axis.text.x = element_text(size = 8, hjust = 1, angle = 45), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| scale_y_continuous(breaks = seq(0, 21, by = 1), limits = c(0,21)) + | |
| scale_fill_manual("legend", values = alpha(c("Intron 7" = "lightgreen", | |
| "Intron 5" = "royalblue4", | |
| "Intron 2" = "red4"), 0.6)) | |
| iqr_cpg_by_region_hip <- iqr_cpg_tissue %>% | |
| filter(Tissue == "HIP") %>% | |
| ggplot(aes(x = reorder(CpG, -IQR), y = IQR, fill = functional_region)) + | |
| geom_col() + | |
| geom_hline(color = "darkred", yintercept = 1) + | |
| labs(y = "IQR", x = "CpG", title = "IQR of DNAm Levels Across CpGs - HIP") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 10), | |
| axis.text.x = element_text(size = 8, hjust = 1, angle = 45), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "bottom") + | |
| scale_y_continuous(breaks = seq(0, 21, by = 1), limits = c(0,21)) + | |
| scale_fill_manual("legend", values = alpha(c("Intron 7" = "lightgreen", | |
| "Intron 5" = "royalblue4", | |
| "Intron 2" = "red4"), 0.6)) | |
| # 13. Correlation matrix of CpGs in the humanized mouse tissues (Supplementary Figure 3) | |
| methylation_humouse_all_tissues <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with("_corrected")) %>% | |
| dplyr::select(sample, Tissue,contains(cpg_locations)) | |
| methylation_humouse_all_tissues_long <- methylation_humouse_all_tissues %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| reduced_df_long_blood <- methylation_humouse_all_tissues_long %>% | |
| filter(Tissue == "Blood", CpG %in% cpgs_remain_blood) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| reduced_df_long_pfc <- methylation_humouse_all_tissues_long %>% | |
| filter(Tissue == "PFC", CpG %in% cpgs_remain_pfc) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| reduced_df_long_hip <- methylation_humouse_all_tissues_long %>% | |
| filter(Tissue == "HIP", CpG %in% cpgs_remain_hip) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| methylation_humouse_all_tissues_corrected <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, Tissue, contains(cpg_locations)) | |
| methylation_humouse_all_tissues_corrected_long <- methylation_humouse_all_tissues_corrected %>% | |
| pivot_longer(cols = "35558386":"35608022", names_to = "CpG", values_to = "methylation") | |
| reduced_df_long_corrected_blood <- methylation_humouse_all_tissues_corrected_long %>% | |
| filter(Tissue == "Blood", CpG %in% cpgs_remain_blood) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| reduced_df_long_corrected_pfc <- methylation_humouse_all_tissues_corrected_long %>% | |
| filter(Tissue == "PFC", CpG %in% cpgs_remain_pfc) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| reduced_df_long_corrected_hip <- methylation_humouse_all_tissues_corrected_long %>% | |
| filter(Tissue == "HIP", CpG %in% cpgs_remain_hip) %>% | |
| left_join(covariates_humouse %>% dplyr::select(-Tissue), by = "sample") | |
| r <- reduced_df_long_blood %>% | |
| pivot_wider(id_cols = sample, names_from = CpG, values_from = methylation) %>% | |
| column_to_rownames("sample") %>% | |
| as.matrix() %>% | |
| rcorr(type = "pearson") | |
| r_pvalue <- r$P | |
| p_corr <- corrplot(r$r, method = "color", type = "upper", p.mat = r_pvalue, insig = "pch", | |
| pch = "ns", sig.level = 0.05, tl.cex = 0.8, pch.cex = 0.5, tl.col = "darkblue", | |
| tl.srt = 45, title = "Blood", mar=c(0,0,2,0)) | |
| r <- reduced_df_long_corrected_pfc %>% | |
| pivot_wider(id_cols = sample, names_from = CpG, values_from = methylation) %>% | |
| column_to_rownames("sample") %>% | |
| as.matrix() %>% | |
| rcorr(type = "pearson") | |
| r_pvalue <- r$P | |
| p_corr <- corrplot(r$r, method = "color", type = "upper", p.mat = r_pvalue, insig = "pch", | |
| pch = "ns", sig.level = 0.05, tl.cex = 0.8, pch.cex = 0.5, tl.col = "darkblue", | |
| tl.srt = 45, title = "Prefrontal Cortex", mar=c(0,0,2,0)) | |
| r <- reduced_df_long_corrected_hip %>% | |
| pivot_wider(id_cols = sample, names_from = CpG, values_from = methylation) %>% | |
| column_to_rownames("sample") %>% | |
| as.matrix() %>% | |
| rcorr(type = "pearson") | |
| r_pvalue <- r$P | |
| p_corr <- corrplot(r$r, method = "color", type = "upper", p.mat = r_pvalue, insig = "pch", | |
| pch = "ns", sig.level = 0.05, tl.cex = 0.8, pch.cex = 0.5, tl.col = "darkblue", | |
| tl.srt = 45, title = "Hippocampus", mar=c(0,0,2,0)) | |
| # 14. Genotype effects on DNAm in humanized mouse and human blood (Supplementary Figure 5) | |
| delta_genotype_humouse_blood <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| filter(Tissue == "Blood", Group %in% c("No Treatment", "V")) %>% # only untreated | |
| dplyr::select(sample, Genotype, contains(c(intron_7, intron_5))) %>% | |
| group_by(Genotype) %>% | |
| summarise(across("35558386":"35578891", | |
| .fns = ~ mean(.x, na.rm = TRUE))) %>% | |
| t() %>% | |
| as.data.frame() %>% | |
| dplyr::slice(-1) %>% | |
| dplyr::rename(`C/G` = "V1", `A/T` = "V2") %>% | |
| mutate(`C/G` = as.numeric(`C/G`), `A/T` = as.numeric(`A/T`), | |
| delta_genotype_humouse = `A/T` - `C/G`) %>% | |
| rownames_to_column("CpG") %>% | |
| dplyr::select(-`A/T`, -`C/G`) %>% | |
| dplyr::rename(`Humanized Mouse` = "delta_genotype_humouse") %>% | |
| t() %>% row_to_names(row_number = 1) | |
| delta_genotype_human_blood <- methylation_human_blood_study2_baseline %>% | |
| dplyr::select(sample, Genotype = rs1360780_T, contains(cpg_locations)) %>% | |
| filter(Genotype %in% c(0, 2)) %>% | |
| group_by(Genotype) %>% | |
| summarise(across("35558386":"35578891", | |
| .fns = ~ mean(.x, na.rm = TRUE))) %>% | |
| t() %>% | |
| as.data.frame() %>% | |
| dplyr::slice(-1) %>% | |
| dplyr::rename(`C/G` = "V1", `A/T` = "V2") %>% | |
| mutate(`C/G` = as.numeric(`C/G`), `A/T` = as.numeric(`A/T`), | |
| delta_genotype_human = `A/T` - `C/G`) %>% | |
| rownames_to_column("CpG") %>% | |
| dplyr::select(-`A/T`, -`C/G`) %>% | |
| dplyr::rename(Human = "delta_genotype_human") %>% | |
| t() %>% row_to_names(row_number = 1) | |
| delta_genotype_human_humouse_blood <- rbind(delta_genotype_humouse_blood, delta_genotype_human_blood) | |
| heatmap_genotype_blood <- delta_genotype_human_humouse_blood %>% | |
| melt() %>% # reshape data as needed for heatmap | |
| mutate(X2 = as.character(X2), value = as.numeric(value)) %>% | |
| dplyr::rename(Percentage = "value") %>% | |
| mutate(Percentage = round(Percentage, digits = 1)) %>% | |
| ggplot(aes(x = X2, y = X1)) + | |
| geom_tile(color = "black", aes(fill = Percentage)) + | |
| scale_fill_gradient2(midpoint = 0, limits = c(-10, 10), space = "Lab", name="DNA methylation [%]") + | |
| geom_text(aes(label = Percentage), size = 5, colour = "black", angle = 90) + | |
| labs(title = Delta ~ "DNA Methylation of Risk Allele Homozygosity - Blood") + | |
| theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14), | |
| axis.text.y = element_text(angle = 90, hjust = 0.5, size = 20), | |
| axis.title = element_blank(), | |
| plot.title = element_text(face = "bold", size = 24), | |
| legend.title = element_text(face = "bold", size = 16), | |
| legend.text= element_text(size=16), | |
| legend.position = "bottom") + | |
| scale_x_discrete(expand=c(0,0)) + scale_y_discrete(expand=c(0,0)) | |
| # 15. Genotype effects on DNAm in humanized mouse and human PFC (Supplementary Figure 6) | |
| delta_genotype_humouse_pfc <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| filter(Tissue == "PFC", Group %in% c("No Treatment", "V")) %>% # only untreated | |
| dplyr::select(sample, Timegroup, Genotype, contains(cpg_locations)) %>% | |
| group_by(Genotype) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = ~ mean(.x, na.rm = TRUE))) %>% | |
| t() %>% as.data.frame() %>% dplyr::slice(-1) %>% | |
| dplyr::rename(`C/G` = "V1", `A/T` = "V2") %>% | |
| mutate(`C/G` = as.numeric(`C/G`), `A/T` = as.numeric(`A/T`), | |
| delta_genotype_humouse = `A/T` - `C/G`) %>% | |
| rownames_to_column("CpG") %>% | |
| dplyr::select(-`A/T`, -`C/G`) %>% | |
| dplyr::rename(`Humanized Mouse` = "delta_genotype_humouse") %>% | |
| t() %>% row_to_names(row_number = 1) | |
| delta_genotype_human_pfc <- human_postmortem_brain_study3_df %>% | |
| dplyr::select(-ends_with("_mval")) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, Genotype = rs1360780_T, contains(cpg_locations)) %>% | |
| filter(Genotype %in% c("CC", "TT")) %>% | |
| group_by(Genotype) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = ~ mean(.x, na.rm = TRUE))) %>% | |
| t() %>% | |
| as.data.frame() %>% | |
| dplyr::slice(-1) %>% | |
| dplyr::rename(`C/G` = "V1", `A/T` = "V2") %>% | |
| mutate(`C/G` = as.numeric(`C/G`), `A/T` = as.numeric(`A/T`), | |
| delta_genotype_human = `A/T` - `C/G`) %>% | |
| rownames_to_column("CpG") %>% | |
| dplyr::select(-`A/T`, -`C/G`) %>% | |
| dplyr::rename(Human = "delta_genotype_human") %>% | |
| t() %>% row_to_names(row_number = 1) | |
| delta_genotype_human_humouse_pfc <- rbind(delta_genotype_humouse_pfc, delta_genotype_human_pfc) | |
| heatmap_genotype_pfc <- delta_genotype_human_humouse_pfc %>% | |
| melt() %>% | |
| mutate(X2 = as.character(X2), value = as.numeric(value)) %>% | |
| dplyr::rename(Percentage = "value") %>% | |
| mutate(Percentage = round(Percentage, digits = 1)) %>% | |
| ggplot(aes(x = X2, y = X1)) + | |
| geom_tile(color = "black", aes(fill = Percentage)) + | |
| geom_text(aes(label = Percentage), size = 5, colour = "black", angle = 90) + | |
| scale_fill_gradient2(midpoint = 0, limits = c(-10, 10), space = "Lab", name="DNA methylation [%]") + | |
| labs(title = Delta ~ "DNA Methylation of Risk Allele Homozygosity - PFC") + | |
| theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14), | |
| axis.text.y = element_text(angle = 90, hjust = 0.5, size = 20), | |
| axis.title = element_blank(), | |
| plot.title = element_text(face = "bold", size = 24), | |
| legend.title = element_text(face = "bold", size = 16), | |
| legend.text= element_text(size=16), | |
| legend.position = "bottom") + | |
| scale_x_discrete(expand=c(0,0)) + scale_y_discrete(expand=c(0,0)) | |
| # 16. Dexamethasone effects on DNAm in humanized mouse and human blood (Supplementary Figure 7) | |
| delta_dex_human_humouse_blood <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| filter(Tissue == "Blood", Timegroup == c("t0", "t4"), | |
| Group %in% c("No Treatment", "DEX")) %>% | |
| dplyr::select(sample, Timegroup, Group, contains(c(intron_7, intron_5))) %>% | |
| group_by(Timegroup, Group) %>% | |
| summarise(across("35558386":"35578891", | |
| .fns = ~ mean(.x, na.rm = TRUE))) %>% | |
| t() %>% | |
| as.data.frame() %>% | |
| dplyr::slice(-1,-2) %>% | |
| dplyr::rename(t0 = "V1", t4 = "V2") %>% | |
| mutate(t0 = as.numeric(t0), t4 = as.numeric(t4), delta_dex_humouse = t4 - t0) %>% | |
| rownames_to_column("CpG") %>% | |
| dplyr::select(-t0, -t4) %>% | |
| left_join(results_wiechmann_df %>% dplyr::select(CpG, delta_dex_human) %>% | |
| mutate(CpG = as.character(CpG))) %>% | |
| dplyr::rename(`Humanized Mouse` = "delta_dex_humouse", Human = "delta_dex_human") %>% | |
| t() %>% row_to_names(row_number = 1) | |
| heatmap_dex_blood <- delta_dex_human_humouse_blood %>% | |
| melt() %>% | |
| mutate(X2 = as.character(X2), value = as.numeric(value)) %>% | |
| dplyr::rename(Percentage = "value") %>% | |
| mutate(Percentage = round(Percentage, digits = 1)) %>% | |
| ggplot(aes(x = X2, y = X1)) + | |
| geom_tile(color = "black", aes(fill = Percentage)) + | |
| scale_fill_gradient2(midpoint = 0, limits = c(-26, 26), space = "Lab", name="DNA methylation [%]") + | |
| geom_text(aes(label = Percentage), size = 5, colour = "black", angle = 90) + | |
| labs(title = Delta ~ "DNA Methylation After Dexamethasone") + | |
| theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14), | |
| axis.text.y = element_text(angle = 90, hjust = 0.5, size = 20), | |
| axis.title = element_blank(), | |
| plot.title = element_text(face = "bold", size = 24), | |
| legend.title = element_text(face = "bold", size = 16), | |
| legend.text= element_text(size=10), | |
| legend.position = "bottom") + | |
| scale_x_discrete(expand=c(0,0)) + scale_y_discrete(expand=c(0,0)) | |
| # 17. Nominal interactions effects of dexamethasone with rs1360780 on DNAm | |
| # in humanized mouse hippocampus (Supplementary Figure 9) | |
| methylation_humouse_all_tissues_complete <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, Tissue, Genotype, Group, Timegroup, contains(cpg_locations)) %>% | |
| mutate(Group = fct_relevel(Group, c("No Treatment", "V", "DEX")), | |
| Timegroup = fct_relevel(Timegroup, c("t0", "t4", "t24")), | |
| Tissue = fct_relevel(Tissue, c("Blood", "PFC", "HIP"))) | |
| intron_5_35570224_effects <- ggplot(methylation_humouse_all_tissues_complete, aes(x = Group, y = `35570224`)) + | |
| geom_boxplot(aes(fill = Genotype), outlier.size = 1) + | |
| geom_point(aes(fill = Genotype), position = position_jitterdodge(jitter.height = 0, jitter.width = 0.2), size = 1) + | |
| facet_grid(Tissue ~ Timegroup, scales = "free") + | |
| scale_fill_brewer(palette = "Paired") + | |
| labs(title = "Intron 5 - 35570224", y = "DNA Methylation [%]") + | |
| theme_bw() + | |
| theme(legend.position = "bottom", | |
| plot.title = element_text(size = 16, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 14), | |
| axis.title.y = element_text(size = 14), | |
| axis.text.x = element_text(size = 12), | |
| axis.text.y = element_text(size = 12, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 1, | |
| linetype = "solid", colour = "darkgray")) + | |
| stat_compare_means(aes(group = Genotype), method = "t.test", label = "p.signif") + | |
| scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) | |
| # 18. DNAm patterns in different tissues of humanized mouse (Figure 2) | |
| intron_7_humouse_all_tissues <- methylation_humouse_all_tissues_mean_sd %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = Tissue, group = Tissue)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("red", "black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 7") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_humouse_all_tissues | |
| intron_5_humouse_all_tissues <- methylation_humouse_all_tissues_mean_sd %>% | |
| filter(CpG %in% intron_5) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = Tissue, group = Tissue)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("red", "black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 5") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = "35570224", ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", xmin = "35578739", xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| intron_5_humouse_all_tissues | |
| intron_2_humouse_all_tissues <- methylation_humouse_all_tissues_mean_sd %>% | |
| filter(CpG %in% intron_2) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = Tissue, group = Tissue)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("red", "black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 2") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "red4", alpha = 0.2) | |
| intron_2_humouse_all_tissues | |
| # 19. DNAm patterns at baseline in blood and PFC of humanized mouse and humans (Figure 3) | |
| intron_7_blood_human_humouse <- methylation_blood_baseline_three_sets_df %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "darkgrey", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 7") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_blood_human_humouse | |
| intron_5_blood_human_humouse <- methylation_blood_baseline_three_sets_df %>% | |
| filter(CpG %in% intron_5) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "darkgrey", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 5") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = "35570224", ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", xmin = "35578739", xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| intron_5_blood_human_humouse | |
| intron_2_blood_human_humouse <- methylation_blood_baseline_three_sets_df %>% | |
| filter(CpG %in% intron_2) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 2") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "red4", alpha = 0.2) | |
| intron_2_blood_human_humouse | |
| intron_7_pfc_humouse_human <- methylation_human_postmortem_humouse_mean_sd %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 7") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_pfc_humouse_human | |
| intron_5_pfc_humouse_human <- methylation_human_postmortem_humouse_mean_sd %>% | |
| filter(CpG %in% intron_5) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 5") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = "35570224", ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", xmin = "35578739", xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| intron_5_pfc_humouse_human | |
| intron_2_pfc_humouse_human <- methylation_human_postmortem_humouse_mean_sd %>% | |
| filter(CpG %in% intron_2) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = study, group = study)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_manual(values=c("black", "dodgerblue3")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 2") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(),legend.text = element_text(size=15), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "red4", alpha = 0.2) | |
| intron_2_pfc_humouse_human | |
| # 20. Age dependent DNAm patterns in blood and PFC between humanized mouse and human (Figure 4) | |
| intron_7_blood_humouse_human_agebins <- methylation_human_humouse_blood_mean_sd_agebins %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = agebin, group = agebin)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_brewer(palette = "Dark2") + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 7") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=10), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_blood_humouse_human_agebins | |
| intron_5_blood_humouse_human_agebins <- methylation_human_humouse_blood_mean_sd_agebins %>% | |
| filter(CpG %in% intron_5) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = agebin, group = agebin)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_brewer(palette = "Dark2") + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 5") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=10), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = "35570224", ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", xmin = "35578739", xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| intron_5_blood_humouse_human_agebins | |
| intron_7_pfc_humouse_human_agebins <- methylation_human_humouse_pfc_mean_sd_agebins %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = agebin, group = agebin)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_brewer(palette = "Dark2") + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 7") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=10), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_pfc_humouse_human_agebins | |
| intron_5_pfc_humouse_human_agebins <- methylation_human_humouse_pfc_mean_sd_agebins %>% | |
| filter(CpG %in% intron_5) %>% | |
| ggplot(aes(x = as.factor(CpG), y = mean, col = agebin, group = agebin)) + | |
| geom_point(alpha = 0.5) + | |
| geom_line(size = 0.7) + | |
| ylim(0,100) + | |
| scale_color_brewer(palette = "Dark2") + | |
| labs(x = "CpG", y = "DNA Methylation [%]", title = "Intron 5") + | |
| theme_bw() + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_text(size = 16), | |
| axis.title.y = element_text(size = 16), | |
| axis.text.x = element_text(size = 14, hjust = 1, angle = 45), | |
| axis.text.y = element_text(size = 14, hjust = 1), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), legend.text = element_text(size=10), | |
| legend.position = "bottom") + | |
| guides(color = guide_legend(override.aes = list(size = 3, alpha = 0.5))) + | |
| annotate("rect", xmin = -Inf, xmax = "35570224", ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) + | |
| annotate("rect", xmin = "35578739", xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| intron_5_pfc_humouse_human_agebins | |
| # 21. Dexamethasone effects on DNAm per CpG in humanized mouse and human blood (Figure 5) | |
| course_human_df <- human_blood_study2_df %>% | |
| pivot_longer(cols = `35558386`:`35578891`, names_to = "CpG", values_to = "value") %>% | |
| mutate(Group = case_when(dex_dichot == "1" ~ "Dexamethasone", | |
| dex_dichot == "0" ~ "Non", | |
| TRUE ~ "error"), | |
| Timegroup = case_when(Dex__0_baseline__1_3h__2_24h_ == "0" ~ "t0", | |
| Dex__0_baseline__1_3h__2_24h_ == "1" ~ "t3", | |
| Dex__0_baseline__1_3h__2_24h_ == "2" ~ "t24", | |
| TRUE ~ "error")) %>% | |
| dplyr::select(CpG, Timegroup, Group, value) %>% | |
| filter(CpG %in% c(intron_7, intron_5)) %>% | |
| group_by(Timegroup, Group, CpG) %>% | |
| summarise(mean = mean(value), sd = sd(value)) %>% | |
| mutate(Timegroup = factor(Timegroup, levels = c("t0", "t3", "t24")), | |
| Group = as.factor(Group)) | |
| intron_5_blood_human_part1 <- course_human_df %>% | |
| filter(CpG %in% c("35569751", "35569757", "35569777", "35569896", "35569922", "35570224")) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| scale_color_manual(values=c("black", "darkblue")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(limits = c(0,14), breaks = seq(0, 14, by = 2)) + | |
| theme_bw() + | |
| facet_wrap(~CpG) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=1) | |
| intron_5_blood_human_part2 <- course_human_df %>% | |
| filter(CpG %in% c("35578739", "35578830", "35578891")) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG, ncol = 2) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=8) | |
| intron_7_blood_human <- course_human_df %>% | |
| filter(CpG %in% intron_7) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| course_mouse_df <- humanized_mouse_df %>% | |
| dplyr::select(-ends_with("_mval"), -ends_with(cpg_locations)) %>% | |
| rename_with(~str_remove(., "_corrected")) %>% | |
| dplyr::select(sample, Tissue, Timegroup, Group, contains(cpg_locations)) %>% | |
| group_by(Tissue, Timegroup, Group) %>% | |
| summarise(across("35558386":"35608022", | |
| .fns = list(mean = ~ mean(.x, na.rm = TRUE), sd = ~ sd(.x, na.rm = TRUE)), | |
| .names = "{.fn}_{.col}")) %>% | |
| pivot_longer(cols = -c(Tissue, Timegroup, Group), names_to = "which" , values_to = "methylation") %>% | |
| separate(which, into = c("which","CpG"), sep = "_") %>% | |
| spread(key=which, value=methylation) %>% | |
| mutate(Group = as.factor(ifelse(Group %in% c("V", "No Treatment"), "Non", "Dexamethasone")), | |
| Timegroup = fct_relevel(Timegroup, c("t0", "t4", "t24"))) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=2) | |
| intron_2_blood_mouse <- course_mouse_df %>% | |
| filter(Tissue == "Blood", CpG %in% intron_2) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group, col = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| scale_color_manual(values=c("black", "darkred")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.title.x = element_blank(), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "red4", alpha = 0.2) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=4) | |
| intron_5_blood_mouse_part1 <- course_mouse_df %>% | |
| filter(Tissue == "Blood", | |
| CpG %in% c("35569751", "35569757", "35569777", "35569896", "35569922", "35570224")) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group, col = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| scale_color_manual(values=c("black", "darkblue")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=1) | |
| intron_5_blood_mouse_part2 <- course_mouse_df %>% | |
| filter(Tissue == "Blood", CpG %in% c("35578739", "35578830", "35578891")) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group, col = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| scale_color_manual(values=c("black", "darkblue")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG, ncol = 2) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "royalblue4", alpha = 0.2) | |
| f <- function(y) seq(floor(min(y)), ceiling(max(y)),by=2) | |
| intron_7_blood_mouse <- course_mouse_df %>% | |
| filter(Tissue == "Blood", CpG %in% intron_7) %>% | |
| ggplot(aes(x = Timegroup, y = mean, group = Group, col = Group)) + | |
| geom_point(alpha = 0.5, position = position_dodge(width = 0.2)) + | |
| scale_color_manual(values=c("black", "darkgreen")) + | |
| geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, position = position_dodge(width = 0.2)) + | |
| labs(y = "Mean DNA Methylation [%]") + | |
| scale_y_continuous(breaks=f) + | |
| theme_bw() + | |
| facet_wrap(~CpG) + | |
| theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), | |
| axis.title.x = element_blank(), | |
| axis.title.y = element_text(size = 16, face = "bold"), | |
| axis.text.x = element_text(size = 14), | |
| axis.text.y = element_text(size = 12), | |
| legend.background = element_rect(fill = "lightgray", size = 0.5, | |
| linetype = "solid", colour = "darkgray"), | |
| legend.title = element_blank(), | |
| legend.position = "none") + | |
| annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "lightgreen", alpha = 0.2) | |
| intron_7_blood_mouse | |
| # 22. Session information | |
| utils:::print.sessionInfo(sessionInfo()[-8]) | |
| # End of script |