From 84148f33411ac61b27a545f4eac7e78223e798ec Mon Sep 17 00:00:00 2001 From: Natan Yusupov Date: Mon, 8 May 2023 13:16:20 +0200 Subject: [PATCH] initial commit upload script and data --- analysis_script.R | 764 +++++++++++++++++++++++++++++++++++++++++ cpgs_amplicon_info.csv | 158 +++++++++ data_df.csv | 96 ++++++ sessioninfo.txt | 1 + 4 files changed, 1019 insertions(+) create mode 100644 analysis_script.R create mode 100644 cpgs_amplicon_info.csv create mode 100644 data_df.csv create mode 100644 sessioninfo.txt diff --git a/analysis_script.R b/analysis_script.R new file mode 100644 index 0000000..f85c85f --- /dev/null +++ b/analysis_script.R @@ -0,0 +1,764 @@ +# Analysis script for the paper: +# "Extensive evaluation of DNA methylation of functional elements in the murine +# Fkbp5 locus using high accuracy DNA methylation measurement via targeted +# bisulfite sequencing" +# +# ============================================================================== +# +# Author: Natan Yusupov, natan_yusupov@psych.mpg.de +# +# Description: The script analyses DNAm data of 157 CpGs from three murine +# tissues/brain regions as described in details in ???"citations to be added"??? +# +# Summary of steps performed in the script "analysis_script.R": +# 1. Load libraries and data +# 2. Label primers with corresponding functional annotations of the locus +# 3. Data preparation: +# A. Order CpGs in genomic order +# B. Separate to DNAm data and covariates data +# C. Transform data to M-Values +# D. Prepare data in long format +# 4. Calculate mean and standard deviation per CpG for each tissue/brain region and ELS status +# 5. Calculate delta mean of DNAm for: FC - Blood, HIP - Blood, HIP - FC +# 6. Plot DNAm patterns across tissues/brain regions (samples) +# 7. Plot DNAm patterns across tissues/brain regions (mean and standard deviation) +# 8. Plot delta DNAm patterns across tissues/brain regions +# 9. Filter CpGs with low variance at baseline (IQR=<1) +# 10. Perform linear regression in each tissue/brain region to study ELS effects on DNAm of each CpG +# 11. Visualize significant CpGs +# +# ============================================================================== + +# 1. Load libraries and data +library(tidyverse) +library(broom) +library(ggpubr) + +data_df <- read.csv("data_df.csv", check.names = F) %>% + mutate(row = as.factor(row), column = as.factor(column), ELS_status = as.factor(ELS_status), + tissue = as.factor(tissue)) +cpgs_amplicon_info <- read.csv("cpgs_amplicon_info.csv") + +# 2. Label primers with corresponding functional annotations of the locus +intron_8 <- cpgs_amplicon_info %>% + filter(amplicon %in% c("11_fk5_gre_i8.1", "11_fk5_gre_i8.2")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +intron_5 <- cpgs_amplicon_info %>% + filter(amplicon %in% c("10_fk5_gre_i5.1", "10_fk5_gre_i5.2", "10_fk5_gre_i5.3","9_fk5_gre_i5")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +intron_1 <- cpgs_amplicon_info %>% + filter(amplicon %in% c("5_fk5_gre_i1", "6_fk5_gre_i1","7_fk5_gre_i1","8_fk5_gre_i1")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +tss <- cpgs_amplicon_info %>% filter(amplicon == "18_fk5_TSS") %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +proximal_enhancer <- cpgs_amplicon_info %>% + filter(amplicon %in% c("1_fk5_gre_pE", "2_fk5_gre_pE_1", "2_fk5_gre_pE_2", "4_fk5_gre_pE")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +ctcf_5UTR <- cpgs_amplicon_info %>% + filter(amplicon %in% c("12_fk5_ctcf_5UTR.1", "12_fk5_ctcf_5UTR.2", "13_fk5_ctcf_5UTR")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +proximal_enhancer_1 <- cpgs_amplicon_info %>% + filter(amplicon == "1_fk5_gre_pE") %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +proximal_enhancer_2 <- cpgs_amplicon_info %>% + filter(amplicon %in% c("2_fk5_gre_pE_1", "2_fk5_gre_pE_2")) %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +proximal_enhancer_3 <- cpgs_amplicon_info %>% + filter(amplicon == "4_fk5_gre_pE") %>% + select(cpg_genomic) %>% pull() %>% sort() %>% as.character() + +# 3. data preparation +# A. Order CpGs in genomic order +target_cpgs <- c(intron_8, intron_5, intron_1, tss, proximal_enhancer, ctcf_5UTR) + +# B. Separate to DNAm data and covariates data +methylation_df <- data_df %>% + dplyr::select(sample, contains(target_cpgs)) %>% + column_to_rownames(var = "sample") + +covariates <- data_df %>% + select(sample, row, column, ELS_status, tissue) + +# C. Transform data to M-Values +methylation_beta <- apply(methylation_df, 2, function(x) x/100) # Beta values +methylation_mval <- apply(methylation_beta, 2, function(x) log2((x)/(1-x))) # M values + +# D. Prepare data in long format +methylation_df_long <- methylation_df %>% + rownames_to_column("sample") %>% + pivot_longer(cols = -sample, names_to = "cpg", values_to = "methylation") %>% + separate(sample, sep = "-", into = c("samplenumber", "tissue"), remove = F) %>% + mutate(functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + functional_region_detailed = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer_1 ~ "Proximal Enhancer: Part 1", + cpg %in% proximal_enhancer_2 ~ "Proximal Enhancer: Part 2", + cpg %in% proximal_enhancer_3 ~ "Proximal Enhancer: Part 3", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + cpg = as.factor(cpg), + tissue = as.factor(tissue)) + +methylation_mval_long <- methylation_mval %>% + as.data.frame() %>% + rownames_to_column("sample") %>% + pivot_longer(cols = -sample, names_to = "cpg", values_to = "methylation") %>% + separate(sample, sep = "-", into = c("samplenumber", "tissue"), remove = F) %>% + mutate(functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + functional_region_detailed = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer_1 ~ "Proximal Enhancer: Part 1", + cpg %in% proximal_enhancer_2 ~ "Proximal Enhancer: Part 2", + cpg %in% proximal_enhancer_3 ~ "Proximal Enhancer: Part 3", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + cpg = as.factor(cpg), + tissue = as.factor(tissue)) + +# 4. Calculate mean and standard deviation per CpG for each tissue/brain region and ELS status +methylation_mean_sd_tissue <- data_df %>% + group_by(tissue) %>% + dplyr::summarise(across(contains(target_cpgs), + .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) %>% + mutate(functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +methylation_mean_sd_status <- data_df %>% + group_by(tissue, ELS_status) %>% + dplyr::summarise(across(contains(target_cpgs), + .fns = list(mean = ~ mean(.x, na.rm = TRUE), + sd = ~ sd(.x, na.rm = TRUE)), + .names = "{.fn}_{.col}"), .groups = "keep") %>% + pivot_longer(cols = -c("tissue","ELS_status"), + names_to = "which" , values_to = "methylation") %>% + separate(which, into = c("which","cpg"), sep = "_") %>% + spread(key = which, value = methylation) %>% + mutate(functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + functional_region_detailed = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer_1 ~ "Proximal Enhancer: Part 1", + cpg %in% proximal_enhancer_2 ~ "Proximal Enhancer: Part 2", + cpg %in% proximal_enhancer_3 ~ "Proximal Enhancer: Part 3", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + cpg = as.factor(cpg)) + +# 5. Calculate delta mean of DNAm for: FC - Blood, HIP - Blood, HIP - FC +delta_mouse <- data_df %>% + filter(ELS_status == "Control") %>% + select(-ELS_status, -sample) %>% + group_by(tissue) %>% + dplyr::summarise(across("28544982":"28665256", + .fns = ~ mean(.x, na.rm = TRUE))) %>% + pivot_longer(cols = -tissue, names_to = "cpg" , values_to = "mean") + +delta_mouse_blood <- delta_mouse %>% + filter(tissue == "Blood") %>% select(cpg, mean_blood = "mean") + +delta_mouse_fc <- delta_mouse %>% + filter(tissue == "FC") %>% select(cpg, mean_fc = "mean") + +delta_mouse_hip <- delta_mouse %>% + filter(tissue == "HIP") %>% select(cpg, mean_hip = "mean") + +delta_final_df <- delta_mouse_blood %>% + left_join(delta_mouse_fc, by = "cpg") %>% + left_join(delta_mouse_hip, by = "cpg") %>% + mutate(delta_fc_blood = mean_fc - mean_blood, + delta_hip_blood = mean_hip - mean_blood, + delta_fc_hip = mean_fc - mean_hip, + functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error")), + functional_region_detailed = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer_1 ~ "Proximal Enhancer: Part 1", + cpg %in% proximal_enhancer_2 ~ "Proximal Enhancer: Part 2", + cpg %in% proximal_enhancer_3 ~ "Proximal Enhancer: Part 3", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +delta_for_plot <- data.frame(cpg = rep(delta_final_df$cpg, 3), + functional_region_detailed = rep(delta_final_df$functional_region_detailed, 3), + delta = c(delta_final_df$delta_fc_blood, + delta_final_df$delta_hip_blood, + delta_final_df$delta_fc_hip), + category = factor(c(rep("FC-Blood", 157), + rep("HIP-Blood", 157), + rep("FC-HIP", 157)), + levels = c("FC-Blood", "HIP-Blood", "FC-HIP"))) + +# 6. Plot DNAm patterns across tissues/brain regions (samples) +color_theme <- c("darkred", "cornflowerblue", "lightgoldenrod3") # set color themes + +plot_per_functional_unit <- function(functional_unit){ # function to make plots + methylation_df_long %>% + filter(functional_region_detailed == functional_unit) %>% + ggplot(aes(x = cpg, y = methylation, col = sample, group = sample)) + + geom_point(alpha = 0.5) + + geom_line(alpha = 0.3) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = paste0("DNA Methylation Patterns Across Tissues: ", functional_unit)) + + ylim(-5, 105) + + facet_wrap(~tissue) + + theme_bw() + + theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 10, face = "bold"), + axis.title.x = element_text(size = 10, face = "bold"), + axis.text.x = element_text(size = 8, hjust = 1, angle = 45), + legend.position = "none") + } + +plot_per_functional_unit("Intron 8") +plot_per_functional_unit("Intron 5") +plot_per_functional_unit("Intron 1") +plot_per_functional_unit("TSS") +plot_per_functional_unit("CTCF 5'UTR") + +methylation_df_long %>% + filter(functional_region == "Proximal Enhancer", tissue == "Blood") %>% + ggplot(aes(x = cpg, y = methylation, col = sample, group = sample)) + + geom_point(alpha = 0.5) + + geom_line(alpha = 0.3) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "DNA Methylation Patterns Blood: Proximal Enhancer") + + ylim(-5, 105) + + theme_bw() + + theme(plot.title = element_text(size = 18, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 8, hjust = 1, angle = 90), + axis.text.y = element_text(size = 8), + legend.position = "none") + +methylation_df_long %>% + filter(functional_region == "Proximal Enhancer", tissue == "FC") %>% + ggplot(aes(x = cpg, y = methylation, col = sample, group = sample)) + + geom_point(alpha = 0.5) + + geom_line(alpha = 0.3) + + labs(x = "CpG", y = "DNAm Methylation [%]", + title = "DNA Methylation Patterns FC: Proximal Enhancer") + + ylim(-5, 105) + + theme_bw() + + theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 8, hjust = 1, angle = 90), + legend.position = "none") + +methylation_df_long %>% + filter(functional_region == "Proximal Enhancer", tissue == "HIP") %>% + ggplot(aes(x = cpg, y = methylation, col = sample, group = sample)) + + geom_point(alpha = 0.5) + + geom_line(alpha = 0.3) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "DNA Methylation Patterns HIP: Proximal Enhancer") + + ylim(-5, 105) + + theme_bw() + + theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 8, hjust = 1, angle = 90), + legend.position = "none") + +# 7. Plot DNAm patterns across tissues/brain regions (mean and standard deviation) +plot_per_functional_unit <- function(functional_unit){ + methylation_mean_sd_status %>% # only unreated samples here + filter(functional_region_detailed == functional_unit, ELS_status == "Control") %>% + ggplot(aes(x = cpg, y = mean, col = tissue, group = tissue)) + + geom_point(alpha = 0.7) + + geom_line(size = 1) + + scale_color_manual(values=color_theme) + + scale_y_continuous(limits = c(0,100), breaks=seq(0,100,10)) + + geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, size = 1) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = functional_unit) + + theme_bw() + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + 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 = "bottom") +} + +plot_per_functional_unit("Intron 8") +plot_per_functional_unit("Intron 5") +plot_per_functional_unit("Intron 1") +plot_per_functional_unit("CTCF 5'UTR") + +methylation_mean_sd_status %>% + filter(functional_region == "TSS", ELS_status == "Control") %>% + ggplot(aes(x = cpg, y = mean, col = tissue, group = tissue)) + + geom_point(alpha = 0.7) + + geom_line(size = 0.4) + + scale_color_manual(values=color_theme) + + geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "TSS") + + theme_bw() + + scale_y_continuous(limits = c(-5,100), breaks=seq(0,100,10)) + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + axis.text.y = element_text(size = 14), + legend.background = element_rect(fill = "lightgray", size = 0.5, + linetype = "solid", colour = "darkgray"), + legend.title = element_blank(), + legend.position = "bottom") + +methylation_mean_sd_status %>% + filter(functional_region_detailed == "Proximal Enhancer: Part 1", ELS_status == "Control") %>% + ggplot(aes(x = cpg, y = mean, col = tissue, group = tissue)) + + geom_point(alpha = 0.7) + + geom_line(size = 1) + + scale_color_manual(values=color_theme) + + geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, size = 1) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "Proximal Enhancer: Part 1") + + theme_bw() + + scale_y_continuous(limits = c(-5,100), breaks=seq(0,100,10)) + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + axis.text.y = element_text(size = 14), + legend.background = element_rect(fill = "lightgray", size = 0.5, + linetype = "solid", colour = "darkgray"), + legend.title = element_blank(), + legend.position = "bottom") + +methylation_mean_sd_status %>% + filter(functional_region_detailed == "Proximal Enhancer: Part 2", ELS_status == "Control") %>% + ggplot(aes(x = cpg, y = mean, col = tissue, group = tissue)) + + geom_point(alpha = 0.7) + + geom_line(size = 1) + + scale_color_manual(values=color_theme) + + geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, size = 1) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "Proximal Enhancer: Part 2") + + theme_bw() + + scale_y_continuous(limits = c(-5,100), breaks=seq(0,100,10)) + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + axis.text.y = element_text(size = 14), + legend.background = element_rect(fill = "lightgray", size = 0.5, + linetype = "solid", colour = "darkgray"), + legend.title = element_blank(), + legend.position = "bottom") + +methylation_mean_sd_status %>% + filter(functional_region_detailed == "Proximal Enhancer: Part 3", ELS_status == "Control") %>% + ggplot(aes(x = cpg, y = mean, col = tissue, group = tissue)) + + geom_point(alpha = 0.7) + + geom_line(size = 1) + + scale_color_manual(values=color_theme) + + geom_errorbar(aes(ymin = mean-sd, ymax = mean+sd), width = .2, size = 1) + + labs(x = "CpG", y = "DNA Methylation [%]", + title = "Proximal Enhancer: Part 3") + + theme_bw() + + scale_y_continuous(limits = c(-5,100), breaks=seq(0,100,10)) + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + axis.text.y = element_text(size = 14), + legend.background = element_rect(fill = "lightgray", size = 0.5, + linetype = "solid", colour = "darkgray"), + legend.title = element_blank(), + legend.position = "bottom") + +# 8. Plot delta DNAm patterns across tissues/brain regions +plot_delta <- function(functional_unit){ + delta_for_plot %>% + filter(functional_region_detailed == functional_unit) %>% + ggplot(aes(x = cpg, y = delta, group = category)) + + geom_line(size = 1, aes(linetype = category, color = category)) + + geom_point(aes(color = category)) + + geom_hline(color = "darkred", yintercept = 0) + + scale_y_continuous(limits = c(-80,80), breaks=seq(-80,80,10)) + + scale_linetype_manual(values=c("solid", "twodash", "dotted")) + + scale_color_manual(values = c("#3B528BFF", "#21908CFF", "#5DC963FF")) + + labs(x = "CpG", y = Delta ~ "DNA Methylation [%]", + title = functional_unit) + + theme_bw() + + 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_text(size = 16, face = "bold"), + axis.text.x = element_text(size = 14, hjust = 1, angle = 45), + 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 = "bottom") +} + +plot_delta("Intron 8") +plot_delta("Intron 5") +plot_delta("Intron 1") +plot_delta("TSS") +plot_delta("Proximal Enhancer: Part 3") +plot_delta("Proximal Enhancer: Part 2") +plot_delta("Proximal Enhancer: Part 1") +plot_delta("CTCF 5'UTR") + +# 9. Filter CpGs with low variance at baseline (IQR=<1) +iqr_cpg_tissue <- data_df %>% + filter(ELS_status == "Control") %>% + select(-c(ELS_status,row, column)) %>% + 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_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +iqr_cpg_tissue %>% + ggplot(aes(x = IQR)) + + geom_histogram() + + geom_vline(color = "darkred", xintercept = 1) + + facet_wrap(~tissue) + + theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 10, face = "bold"), + axis.title.x = element_text(size = 10, face = "bold"), + axis.text.x = element_text(size = 5, hjust = 1, angle = 45), + legend.background = element_rect(fill = "lightgray", size = 0.5, + linetype = "solid", colour = "darkgray"), + legend.title = element_blank(), + legend.position = "bottom") + + theme_bw() + + scale_x_continuous(breaks = seq(0, 25, by = 1)) + + scale_y_continuous(breaks = seq(0, 100, by = 10)) + +cpgs_remain_blood <- iqr_cpg_tissue %>% filter(tissue == "Blood", IQR > 1) %>% pull(cpg) +print(paste0("CpGs remained for final analysis in blood: ", length(cpgs_remain_blood))) +cpgs_remain_fc <- iqr_cpg_tissue %>% filter(tissue == "FC", IQR > 1) %>% pull(cpg) +print(paste0("CpGs remained for final analysis in FC: ", length(cpgs_remain_fc))) +cpgs_remain_hip <- iqr_cpg_tissue %>% filter(tissue == "HIP", IQR > 1) %>% pull(cpg) +print(paste0("CpGs remained for final analysis in HIP: ", length(cpgs_remain_hip))) + +reduced_df_mval_long_blood <- methylation_mval_long %>% filter(tissue == "Blood", cpg %in% cpgs_remain_blood) +reduced_df_mval_long_fc <- methylation_mval_long %>% filter(tissue == "FC", cpg %in% cpgs_remain_fc) +reduced_df_mval_long_hip <- methylation_mval_long %>% filter(tissue == "HIP", cpg %in% cpgs_remain_hip) +reduced_df_mval_all_tissues <- rbind(reduced_df_mval_long_blood, reduced_df_mval_long_fc, reduced_df_mval_long_hip) + +# 10. Perform linear regression in each tissue/brain region to study ELS effects on DNAm of each CpG +lm_summary <- function(tissue_region){ # function to run linear regressions in each tissue separately + data_sub <- reduced_df_mval_all_tissues %>% + pivot_wider(id_cols = sample, names_from = cpg, values_from = methylation) %>% + left_join(covariates %>% select(sample, tissue, ELS_status)) %>% + filter(tissue == tissue_region) + results <- list() + for(i in outcome){ + f <- as.formula(paste0("`", i, "`", "~ ELS_status")) + tidy <- tidy(lm(formula = f, data = data_sub, na.action = na.omit)) + results[[i]] <- tidy + } + results +} + +## Blood +outcome <- cpgs_remain_blood %>% sort() + +lm_blood <- lm_summary("Blood") +lm_blood_df <- purrr::map_dfr(lm_blood, ~ .x, .id = "cpg") %>% + mutate(q.value = p.adjust(p.value, method = "fdr"), + p.signif = case_when(p.value < 0.05 & p.value >= 0.01 ~ "*", + p.value < 0.01 & p.value >= 0.001 ~ "**", + p.value < 0.001 & p.value >= 0.0001 ~ "***", + p.value < 0.0001 ~ "****", + p.value > 0.05 ~ "ns"), + q.signif = case_when(q.value < 0.05 & q.value >= 0.01 ~ "*", + q.value < 0.01 & q.value >= 0.001 ~ "**", + q.value < 0.001 & q.value >= 0.0001 ~ "***", + q.value < 0.0001 ~ "****", + q.value > 0.05 ~ "ns"), + functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +lm_blood_df %>% filter(term != "(Intercept)", (p.signif != "ns" | q.signif != "ns")) + +## Frontal cortex +outcome <- cpgs_remain_fc %>% sort() + +lm_fc <- lm_summary("FC") +lm_fc_df <- purrr::map_dfr(lm_fc, ~ .x, .id = "cpg") %>% + mutate(q.value = p.adjust(p.value, method = "fdr"), + p.signif = case_when(p.value < 0.05 & p.value >= 0.01 ~ "*", + p.value < 0.01 & p.value >= 0.001 ~ "**", + p.value < 0.001 & p.value >= 0.0001 ~ "***", + p.value < 0.0001 ~ "****", + p.value > 0.05 ~ "ns"), + q.signif = case_when(q.value < 0.05 & q.value >= 0.01 ~ "*", + q.value < 0.01 & q.value >= 0.001 ~ "**", + q.value < 0.001 & q.value >= 0.0001 ~ "***", + q.value < 0.0001 ~ "****", + q.value > 0.05 ~ "ns"), + functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +lm_fc_df %>% filter(term != "(Intercept)", (p.signif != "ns" | q.signif != "ns")) + +## Hippocampus +outcome <- cpgs_remain_hip %>% sort() + +lm_hip <- lm_summary("HIP") +lm_hip_df <- purrr::map_dfr(lm_hip, ~ .x, .id = "cpg") %>% + mutate(q.value = p.adjust(p.value, method = "fdr"), + p.signif = case_when(p.value < 0.05 & p.value >= 0.01 ~ "*", + p.value < 0.01 & p.value >= 0.001 ~ "**", + p.value < 0.001 & p.value >= 0.0001 ~ "***", + p.value < 0.0001 ~ "****", + p.value > 0.05 ~ "ns"), + q.signif = case_when(q.value < 0.05 & q.value >= 0.01 ~ "*", + q.value < 0.01 & q.value >= 0.001 ~ "**", + q.value < 0.001 & q.value >= 0.0001 ~ "***", + q.value < 0.0001 ~ "****", + q.value > 0.05 ~ "ns"), + functional_region = as.factor(case_when( + cpg %in% intron_8 ~ "Intron 8", + cpg %in% intron_5 ~ "Intron 5", + cpg %in% intron_1 ~ "Intron 1", + cpg %in% tss ~ "TSS", + cpg %in% proximal_enhancer ~ "Proximal Enhancer", + cpg %in% ctcf_5UTR ~ "CTCF 5'UTR", + TRUE ~ "error"))) + +lm_hip_df %>% filter(term != "(Intercept)", (p.signif != "ns" | q.signif != "ns")) + +# 11. Visualize significant CpGs + +## Blood +data_df %>% + ggplot(aes(x = ELS_status, y = `28557969`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title = "Intron 5 - 28557969", y = "DNA Methylation [%]") + + theme_bw() + + 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 = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28557969`, na.rm = T)), + round(max(data_df$`28557969`, na.rm = T)), by = 4)) + +data_df %>% + ggplot(aes(x = ELS_status, y = `28579496`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title ="Intron 1 - 28579496", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28579496`, na.rm = T)), + round(max(data_df$`28579496`, na.rm = T)), by = 4)) + +## FC +data_df %>% + ggplot(aes(x = ELS_status, y = `28557729`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title = "Intron 5 - 28557729", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28557729`, na.rm = T)), + round(max(data_df$`28557729`, na.rm = T)), by = 4)) + +data_df %>% + ggplot(aes(x = ELS_status, y = `28649771`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title ="Proximal Enhancer - 28649771", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28649771`, na.rm = T)), + round(max(data_df$`28649771`, na.rm = T)), by = 4)) + +data_df %>% + ggplot(aes(x = ELS_status, y = `28649785`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title ="Proximal Enhancer - 28649785", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28649785`, na.rm = T)), + round(max(data_df$`28649785`, na.rm = T)), by = 4)) + +## HIP +data_df %>% + ggplot(aes(x = ELS_status, y = `28657196`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title = "CTCF 5'UTR - 28657196", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28657196`, na.rm = T)), + round(max(data_df$`28657196`, na.rm = T)), by = 4)) + +data_df %>% + ggplot(aes(x = ELS_status, y = `28657385`)) + + geom_boxplot(aes(fill = ELS_status), alpha = 0.7, outlier.size = 1) + + geom_point(aes(fill = ELS_status), alpha = 0.7, + position = position_jitterdodge(jitter.height = 0, + jitter.width = 0.2), size = 1) + + facet_wrap(~ tissue) + + scale_fill_manual(values = c("snow2", "seagreen4")) + + labs(title = "CTCF 5'UTR - 28657385", y = "DNA Methylation [%]") + + theme_bw() + + theme(plot.title = element_text(size = 20, face = "bold", hjust = 0.5), + axis.title.y = element_text(size = 16, face = "bold"), + axis.title.x = element_blank(), + axis.text.x = element_text(size = 16, face = "bold"), + axis.text.y = element_text(size = 14), + legend.position = "none") + + stat_compare_means(aes(group = ELS_status, label = paste0("p = ", ..p.format.., ..p.signif..)), + method = "t.test", size = 5) + + scale_y_continuous(breaks = seq(round(min(data_df$`28657385`, na.rm = T)), + round(max(data_df$`28657385`, na.rm = T)), by = 2)) + +# Session information +utils:::print.sessionInfo(sessionInfo()[-8]) + +# End of script diff --git a/cpgs_amplicon_info.csv b/cpgs_amplicon_info.csv new file mode 100644 index 0000000..0ccc1eb --- /dev/null +++ b/cpgs_amplicon_info.csv @@ -0,0 +1,158 @@ +"amplicon","cpg_genomic" +"1_fk5_gre_pE",28660081 +"1_fk5_gre_pE",28660099 +"1_fk5_gre_pE",28660105 +"1_fk5_gre_pE",28660114 +"1_fk5_gre_pE",28660118 +"1_fk5_gre_pE",28660136 +"1_fk5_gre_pE",28660139 +"1_fk5_gre_pE",28660142 +"1_fk5_gre_pE",28660148 +"1_fk5_gre_pE",28660155 +"1_fk5_gre_pE",28660161 +"1_fk5_gre_pE",28660163 +"1_fk5_gre_pE",28660177 +"1_fk5_gre_pE",28660182 +"1_fk5_gre_pE",28660187 +"1_fk5_gre_pE",28660192 +"1_fk5_gre_pE",28660194 +"1_fk5_gre_pE",28660206 +"1_fk5_gre_pE",28660210 +"1_fk5_gre_pE",28660217 +"1_fk5_gre_pE",28660223 +"1_fk5_gre_pE",28660225 +"1_fk5_gre_pE",28660230 +"1_fk5_gre_pE",28660253 +"1_fk5_gre_pE",28660259 +"1_fk5_gre_pE",28660261 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+"29-Blood",16.17,20.48,11.3,76.98,79.8,79.33,4.41,19.85,2,9.22,6.22,8.31,31.98,12.71,19.04,55.66,40.43,40.84,29.86,86.82,82.59,83.85,76.91,91.52,93.4,93.31,90.17,91.99,NA,NA,NA,NA,96.02,91.47,96.4,95.23,92.52,92.67,86.62,85.53,92.44,98.01,77.64,72.78,68.67,80.45,82.38,97.45,93.15,83.72,0.14,0.34,0.26,0.21,0.18,0.2,0.26,0.34,0.29,0.3,0.18,0.37,0.38,0.25,0.46,0.32,0.21,82.67,56.91,74,53.76,78.13,54.2,88.49,0.25,0.23,0.21,0.14,0.34,0.26,0.21,0.11,0.2,0.1,0.26,0.84,0.21,0.24,0.16,0.19,0.11,0.2,0.2,0.36,0.26,0.35,0.41,0.27,0.12,0.18,0.46,0.34,0.1,0.3,0.4,0.14,0.23,0.32,0.19,0.19,0.26,74.66,0.48,0.26,0.32,0.16,0.36,0.26,0.03,0.17,0.14,0.1,0.2,0.33,0.28,0.24,0.2,0.22,0.31,0.09,0.15,0,0,0.11,0.06,0.2,0.07,0.23,0.22,0.22,0.22,0.16,0.19,0.13,52.63,84.06,94.46,97.56,46.94,5.33,0.32,0.23,1.09,0.34,0.34,1.01,0.81,"A","12","Control","Blood" +"31-Blood",14.56,13.34,8.59,64.03,77.5,75.68,3.58,16.91,2.05,10.47,6.64,3.9,29.74,8.96,17.99,56.61,47.18,38.86,31.87,87.29,79.5,69.43,63.39,92.93,96.46,94.15,86.5,88.35,74.9,70.23,93.01,65.62,95.06,92.52,93.05,94.82,94.98,90.61,89.12,91.61,92.25,91.46,79.07,71.8,52.2,71.13,70.18,94.34,94.68,84.32,0.18,0.44,0.23,0.28,0.19,0.35,0.48,0.21,0.31,0.23,0.34,0.37,0.32,0.26,0.29,0.26,0.36,84.38,51.18,85.73,59.31,90.9,60.89,89.02,0.21,0.33,0.14,0.4,0.58,0.46,0.42,0.3,0.14,0.28,0.27,0.18,0.18,0.25,0.12,0.12,0.14,0.21,0.19,1.86,0.16,0.27,0.54,0.16,0.36,0.29,0.24,0.26,0.25,0.21,0.34,0.11,0.31,0.22,0.28,0.15,0.5,72.58,0.16,0.45,0.12,0.28,0.49,0.44,0.2,0.5,0.4,0.22,0.2,0.32,0.18,0.09,0.39,0.43,0.31,0.12,0.09,0,0.14,0.13,0.37,0.17,0.09,0.17,0.2,0.24,0.2,0.2,0.32,0.16,42.32,83.24,94.49,95.7,42.57,4.45,0.16,0.26,1.17,0.3,0.45,1.22,1.28,"E","12","Control","Blood" +"33-Blood",17.95,15.87,7.43,71.51,80.5,76.09,4.94,19.71,2.27,9.59,4.57,4.75,33.59,12.13,21.67,54.63,43.35,43.41,31.46,85.98,80.77,77.96,70.05,92.3,96.09,93.72,92.08,95.16,77.55,75.84,93.29,67.49,94.63,93.98,94.47,96.46,92.96,78.38,77.8,76.22,95.76,93.23,68.48,67.62,58.25,78.91,75.94,95.34,91.8,81.62,0.22,0.2,0.34,0.31,0.18,0.3,0.42,0.42,0.24,0.42,0.4,0.37,0.28,0.26,0.19,0.27,0.29,76.09,51.71,82.47,59.84,85.94,63.35,92.4,0.35,0.07,0.2,0.25,0.28,0.34,0.12,0.3,0.27,0.23,0.21,0.32,0.13,0.17,0.21,0.32,0.26,0.33,0.16,0.3,0.3,0.27,0.34,0.22,0.26,0.17,0.29,0.35,0.3,0.26,0.28,0.13,0.28,0.24,0.17,0.15,0.35,70.38,0.22,0.17,0.06,0.11,0.17,0.16,0.11,0.06,0.06,0.24,0.17,0.49,0.42,0.3,0.06,0.13,0,0.31,0,0.24,0,0.09,0,0.29,0.06,0.06,0.16,0.32,0.49,0.16,0.27,0.86,45.61,82.98,96.82,95.74,42.64,4.37,0.19,0.29,1.67,0.35,0.49,1.35,0.87,"F","9","Control","Blood" +"35-Blood",14.19,18.97,9.22,70.54,77.55,73.31,3.36,19.66,2.21,12.57,7.71,6.84,28.55,10.23,17.97,54.83,43.01,41.32,28.51,87.43,80.32,72.47,66.95,92.8,93.84,91.09,91.04,94.63,NA,NA,NA,NA,97.23,94.78,94.33,94.35,93.69,90.44,88.33,82.02,85.69,94.23,72.2,50,45.96,75.14,73.73,94.24,96.37,86.04,0.18,0.27,0.29,0.22,0.29,0.21,0.34,0.46,0.3,0.43,0.28,0.27,0.31,0.28,0.3,0.23,0.28,80.93,49.48,67.17,59.16,78.32,48.82,79.98,0.24,0.22,0.24,0.27,0.34,0.32,0.2,0.22,0.3,0.23,0.29,0.2,0.14,0.31,0.28,0.18,0.1,0.16,0.1,0.2,0.21,0.22,0.26,0.32,0.16,0.19,0.28,0.34,0.13,0.33,0.28,0.19,0.43,0.68,0.35,0.14,0.48,73.54,0.26,0.42,0.21,0.2,0.11,0.15,0.21,0.21,0.28,0.27,0.26,0.23,0.28,0.17,0.38,1.34,0.13,0,0,0.44,0.09,0.09,0,0,0.28,0.26,0.25,0.3,0.3,0.15,0.15,0.15,46.03,84.15,93.61,95.18,37.16,4.03,0.2,0.42,0.7,0.31,0.4,1.48,0.5,"B","12","ELS","Blood" +"37-Blood",17.45,19.41,7.62,80.98,86.08,82.01,3.72,18.59,2.4,10.75,5.39,6.27,31.69,10.95,19.41,53.63,41.75,43.51,27.86,87.84,82.08,85.71,74.78,95.88,92.04,93.78,91.16,88.98,82.89,79.49,95.7,67.36,95.72,93.5,92.81,95.05,94.65,92.52,87.38,78.95,90.98,99.1,75.48,73.87,60.8,85.05,85.33,94.5,97.89,88.54,0.22,0.42,0.23,0.3,0.18,0.5,0.38,0.44,0.36,0.31,0.24,0.32,0.18,0.47,0.25,0.25,0.19,67.58,51.26,84.07,60.54,85.5,62.1,98.23,0.21,0.16,0.21,0.22,0.34,0.21,0.23,0.15,0.11,0.22,0.17,0.25,0.13,0.19,0.13,0.21,0.15,0.34,0.21,0.27,0.35,0.36,0.28,0.42,0.37,0.22,0.42,0.25,0.38,0.22,0.28,0.33,0.43,0.26,0.17,0.11,0.4,72.38,0.39,0.33,0.06,0.19,0.13,0.13,0.07,0.33,0.36,0.21,0.2,0.22,0.21,0.21,0.27,0,0.15,0.16,0,0,0.11,0.11,0,0,0.07,0.07,0.32,0.26,0.25,0.13,0.06,0.38,53.81,83.49,93.83,96.15,50.41,2.74,0.13,0.2,2.96,0.33,0.33,0.77,1.02,"G","11","ELS","Blood" +"39-Blood",17.99,18.94,8.53,76.19,80.13,79.05,4.4,18.09,2.18,9.55,6.93,3.02,31.94,12.09,19.34,52.91,42.65,42.14,29.12,87.19,82.29,81.88,67.02,92.98,96.71,92.03,89.55,95.81,81.35,77.25,94.4,68.94,93.85,92.03,94.2,96,93.16,94.94,89.25,85.03,92.96,92.7,69.94,71.63,51.18,79.37,81.11,95.02,94.73,89.29,0.21,0.28,0.61,0.34,0.18,0.3,0.17,0.44,0.27,0.31,0.4,0.28,0.14,0.36,0.32,0.18,0.14,75.83,57.99,85.84,67.69,92.42,69.25,93.2,0.34,0.1,0.21,0.26,0.13,0.29,0.13,0.34,0.16,0.22,0.21,0.26,0.19,0.13,0.26,0.37,0.21,0.24,1.36,0.29,0.33,0.29,0.3,0.15,0.19,0.14,0.17,0.4,0.49,0.3,0.33,0.21,0.36,0.59,0.3,0.19,0.31,72.57,0.23,0.36,0.12,0.06,0.12,0.35,0.06,0,0.72,0.26,0.06,0.4,0.32,0.13,0.19,0.07,0,0,0,0,0.1,0.09,0,0.12,0.13,0.18,0.17,0.11,0.06,0.23,0.23,0.29,46.31,87.17,95.37,95.4,44.66,2.65,0.13,0.93,1.9,0.21,0.29,0.77,0.82,"D","12","ELS","Blood" +"41-Blood",14.4,17.17,10.02,69.57,80.67,77.26,3.89,18.53,2.33,9.66,5.2,4.82,33.61,10.68,17.93,56.08,44.45,44.33,29.95,87.88,82.85,77.02,68.86,96.56,92.36,88.24,94.31,94.31,77.73,73.78,93.89,70.9,96.11,90.38,95.26,95.22,93.82,87.95,94.07,82.42,91.22,94.74,80.32,75.38,61.59,75.49,76.5,96.68,94.78,86.07,0.21,0.31,0.3,0.29,0.19,0.51,0.38,0.33,0.3,0.33,0.21,0.28,0.3,0.19,0.22,0.31,0.24,74.35,58.1,72.5,54.93,79.76,48.77,89.84,0.35,0.17,0.36,0.79,0.2,0.36,0.11,0.14,0.17,0.22,0.19,0.17,0.14,0.66,0.22,0.17,0.16,0.79,1.04,0.18,0.27,0.31,0.63,0.2,0.12,0.33,0.26,0.17,0.2,0.26,0.42,0.16,0.2,0.26,0.38,0.3,0.32,70.53,0.38,0.39,0.27,0.32,0.11,0.22,0.11,0.06,0.18,0.35,0.16,0.31,0.3,0.12,0.29,0.25,0.13,0,0.12,0,0.1,0,0.1,0.06,0.18,0.73,0.27,0.11,0.43,0.11,0.37,0.11,51.76,84.63,93.96,96.03,43.27,2.48,0.18,0.5,0.98,0.24,0.57,0.75,0.74,"E","9","ELS","Blood" +"43-Blood",14.37,18.45,8.51,72.21,80.5,77.03,4.02,20.81,1.98,9.61,5.96,5.83,28.84,11.56,17.25,53.81,41.19,43.86,32.13,87.14,82.19,80.98,68.78,95.95,91.54,91.03,90.82,95.19,75.7,75.59,92.6,65.13,95,93.75,93.47,94.55,94.44,90.15,90.7,86.18,87.12,96.52,74.95,64.27,55.67,75.03,74.12,97.14,94.86,84.69,0.36,0.28,0.25,0.27,0.31,0.19,0.21,0.33,0.21,0.31,0.21,0.26,0.23,0.23,0.29,0.3,0.24,77.35,53.17,75.3,49.44,76.49,54.34,87.74,0.26,0.15,0.09,0.27,0.29,0.26,0.27,0.18,0.14,0.23,0.25,0.2,0.17,0.32,0.08,0.22,0.13,0.19,0.2,0.24,0.2,0.32,0.3,0.42,0.19,0.24,0.29,0.27,0.18,0.52,0.42,0.13,0.14,0.36,0.34,0.31,0.27,71.43,0.17,0.17,0.17,0.11,1.41,0.33,0.28,0.11,0.37,0.06,0.28,0.25,0.62,0.12,0.17,0.07,0.12,0,0,0,0.21,0,0.11,0.06,0.06,0.12,0.05,0.11,0.22,0.32,0.05,0.16,47.67,82.64,93.63,96.01,42.56,1.75,0.19,0.72,1.33,0.32,0.34,0.62,0.9,"D","11","ELS","Blood" +"45-Blood",13.4,15.13,6.68,66.71,77.74,76.27,4.47,18.23,1.29,8.5,5.81,6.01,26.05,11.3,16.68,55.98,46.59,41.58,31.83,89.27,84.31,70.97,66.48,95.47,95.18,89.85,93.63,93.91,NA,NA,NA,NA,95.01,93.61,95.38,94.58,92.66,87.09,96.4,76.63,94.07,97.54,76.23,72.08,56.02,73.3,76.65,95.34,93.84,86.3,0.15,0.3,0.38,0.28,0.3,0.39,0.27,0.48,0.33,0.32,0.26,0.25,0.13,0.35,0.39,0.3,0.25,82.36,55.28,78.11,59.21,85.26,56.95,85.78,0.29,0.09,0.19,0.31,0.59,0.25,0.26,0.13,0.2,0.39,0.21,0.2,0.22,0.14,0.21,0.21,0.15,0.2,0.24,0.49,0.22,0.27,0.22,0.2,0.27,0.21,0.29,0.39,0.24,0.28,0.29,0.16,0.32,0.12,0.23,0.19,0.31,73.3,0.2,0.2,0.28,0.12,0.16,0.32,0.16,0.21,0.26,0.21,0.49,0.45,0.35,0.44,0.21,0.24,0.5,0,0.09,0.17,0,0.53,0,0.12,0.09,0.74,0.24,0.16,0.31,0.15,0.54,0.16,42.98,85.43,94.2,96.27,38.46,4.14,0.3,0.53,1.28,0.54,0.42,0.52,0.99,"G","12","Control","Blood" +"47-Blood",15.7,14.53,9.47,70.21,78.41,73.47,7.23,23.57,2.22,13.47,7.65,5.92,28.17,12.19,17.74,51.67,45.19,38.27,29.37,86.73,79.9,80.09,70.85,95.75,95.91,93.11,88.7,93.67,77.51,75.07,92.55,68.09,95.27,93.93,94.2,93.36,95.65,89.73,94.46,85.83,96.73,98.81,79.36,72.67,61.04,78.95,75.46,95.09,94.37,83.23,0.22,0.29,0.33,0.24,0.18,0.33,0.49,0.25,0.19,0.36,0.38,0.27,0.23,0.23,0.24,0.33,0.38,84.79,52.3,74.05,59.51,81.32,50.07,88.03,0.46,0.12,0.13,0.39,0.38,0.25,0.23,0.18,0.17,0.25,0.36,0.23,0.1,0.12,0.19,0.24,0.2,0.2,0.27,0.32,0.18,0.25,0.3,0.18,0.13,0.22,0.41,0.31,0.24,0.38,0.19,0.17,0.21,0.31,0.3,0.16,0.4,70.42,0.07,0.26,0.13,0.06,0.2,0.13,0.13,0.2,0.29,0.07,0.33,0.22,0.28,0.36,0.14,0.15,0.15,0.17,0,0,0,0,0,0,0.15,0.27,0.19,0.13,0.19,0.13,0.31,0.06,45.9,87.95,93.15,95.68,41.45,2.7,0.12,0.66,0.68,0.27,0.66,0.7,1.21,"D","9","Control","Blood" +"5-Blood",10.57,10.71,3.68,66.74,76.29,71.88,3.54,19.73,2.9,10.57,6.07,6.99,28.86,9.52,18.92,54.04,43.92,42.54,31.24,86.04,79.5,73.9,65.45,93.89,92.29,89.65,89.84,94.1,70.64,67.94,92.5,66.39,94.75,92.99,95.1,92.56,91.73,89.14,92.15,89.82,89.44,98.5,72.63,73.76,61.65,NA,NA,NA,NA,NA,0.23,0.19,0.43,0.3,0.3,0.23,0.36,0.19,0.26,0.27,0.3,0.38,0.37,0.28,0.3,0.4,0.29,71.53,61.7,84.14,52.87,91.27,65.54,93.81,0.2,0.22,0.15,0.22,0.2,0.22,0.23,0.13,0.19,0.28,0.21,0.17,0.15,0.16,0.2,0.24,0.15,0.29,0.12,0.27,0.33,0.18,0.28,0.15,0.27,0.11,0.49,0.26,0.22,0.33,0.62,0.18,0.08,0.35,0.23,0.17,0.42,71.89,0.31,0.45,0.12,0.12,0.56,0.18,0.25,0.06,0.13,0.26,0.06,0.14,0.21,0.2,0.13,0.07,0.31,0.49,0.13,0,0,0,0,0.26,0,0.25,0.24,0.3,0.61,0.24,0.3,0.18,41.32,85.42,94.64,93.23,37.75,3.82,0.24,0.96,0.52,NA,NA,NA,NA,"C","9","ELS","Blood" +"63-Blood",15.3,17.03,6.5,78.17,85.04,79.08,3.09,21.38,2.39,10.06,5.51,4.61,29.8,12.87,19.8,56.34,44.27,42.37,29.01,86.69,81.94,81.36,76.61,93.08,96.13,90.59,93.96,87.29,79.84,76.56,93.55,69.3,96.67,95.52,94.85,94.77,95,96.76,95.1,87.66,89.38,92.95,72.18,74.16,49.89,84.82,85.19,98.22,95.93,90.25,0.38,0.24,0.32,0.29,0.19,0.33,0.35,0.4,0.19,0.24,0.3,0.3,0.23,0.27,0.23,0.26,0.32,76.63,56.56,88.38,74.51,90.4,64.95,86.47,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0.2,0.22,0.46,0.36,0.37,0.49,0.47,0.36,0.3,0.33,0.22,0.27,0.76,0.41,0.22,0.39,71.98,0.25,0.32,0.13,0.43,0.51,0.12,0.32,0.73,0.28,0.2,0.06,0.43,0.41,0.27,0.2,0.07,0.28,0.15,0.14,0.13,0,0,0,0.27,0.55,0.07,0.31,0.36,0.31,0.73,0.24,0.18,50.79,84.97,93.95,94.3,44.5,2.1,0.33,0.49,4.13,0.29,0.24,0.68,0.72,"H","10","Control","Blood" +"65-Blood",17,18.81,11.65,76.94,83.92,79.21,5.14,14.29,1.27,6.75,6.27,4.03,32.35,12.2,19.87,54.29,41.17,38.76,29.93,85.82,79.12,82.1,75.63,92.11,94.39,91.37,86.58,94.07,80.74,79.56,94.41,65.25,96.75,93.44,92.77,92.93,95.59,86.93,89.62,85.92,86.85,98.88,84.58,62.92,72.03,87.32,81.06,97.7,93.96,90.41,0.17,0.3,0.36,0.26,0.21,0.21,0.33,0.15,0.28,0.4,0.32,0.3,0.26,0.21,0.35,0.35,0.31,78.63,58.02,69.41,59.82,89.57,58.44,91.86,0.17,0.18,0.18,0.24,0.25,0.21,0.15,0.11,0.25,0.15,0.36,0.18,0.2,0.31,0.14,0.14,0.15,0.09,0.27,0.29,1.02,0.19,0.46,0.24,0.19,0.12,0.31,0.23,0.21,0.34,0.21,0.17,0.31,0.19,0.41,0.26,0.37,71.69,1.22,0.48,0.17,0.17,0.12,0.63,0.12,0.24,0.06,0.25,0.47,0.2,0.13,0.06,0.24,0.2,0,0.14,0.13,0,0,0,0.11,0.06,0.13,0,0.11,0.11,0.28,0.23,0.34,0.28,49.16,85.81,94.37,95.11,41.63,4.57,0.18,0.39,0.83,0.44,0.2,1.1,0.62,"F","11","Control","Blood" +"67-Blood",18.3,16.86,9.46,77.4,85.16,80.02,4.27,19.13,3.12,12.31,7.22,5.42,31.41,10.62,18.27,55.51,42.52,41.3,29.36,87.61,80.62,84.2,71.16,94.58,96.67,89.71,87.32,94.26,81.99,80.42,93.71,67.83,95.22,93.37,94.05,94.43,93.8,82.05,89.56,94.23,95.4,97.27,88.73,83.96,73.89,86.89,85.24,96.76,95.32,92.31,0.14,0.39,0.19,0.41,0.19,0.23,0.35,0.31,0.16,0.33,0.29,0.19,0.34,0.34,0.4,0.44,0.28,69.48,55.18,83.6,59.98,85.44,68.85,87.53,0.46,0.57,0.12,0.2,0.22,0.22,0.83,0.04,0.22,0.19,0.35,0.5,0.06,0.29,0.18,0.16,0.12,0.12,0.24,0.34,0.29,0.35,0.24,0.26,0.29,0.13,0.33,0.45,0.37,0.41,0.28,0.17,0.26,0.13,0.27,0.13,0.43,71.32,0.12,0.44,0.12,0.06,0.06,0.18,0,0.13,0.28,0.27,0.13,0.28,0.35,0.54,1.96,0.29,0,0.56,0.13,0.13,0.11,0,0,0.2,0,0.07,0.31,0.31,0.12,0.18,0.3,0.24,50.18,84.38,94.83,96.52,44.04,2.62,0.24,0.2,0.99,0.47,0.33,1.31,0.48,"E","11","Control","Blood" +"69-Blood",31.29,13.03,13.36,82.01,80.63,76.6,6.93,21.85,3.43,14.29,9.27,5.11,29.78,11.08,18.84,48.81,36.41,38.22,24.3,84.22,79.98,82.91,77.23,94.23,93.4,92.11,89.03,79.31,77.93,72.98,92.89,62.12,97.07,95.18,91.77,95.32,94.96,89.34,93,84.05,93,83.8,65.94,67.46,62.53,79.58,80.93,95.87,95.15,88.19,0.23,0.23,0.37,0.24,0.16,0.37,0.47,0.29,0.17,0.34,0.3,0.16,0.33,0.22,0.39,0.28,0.29,81.1,63.45,81.36,56.1,82.6,73.38,84.07,0.2,0.13,0.13,0.22,0.27,0.24,0.35,0.14,0.11,0,0.13,0.2,0.13,0.13,0.07,0.18,0.16,NA,0.24,0.18,0.15,0.28,0.23,0.18,0.2,0.57,0.39,0.22,0.21,0.3,0.24,0.11,0.29,0.2,0.69,0.78,0.31,72.43,0.94,0.53,0.43,0.34,0.18,0.08,0.09,0,0.29,0.19,0.18,0.5,0.29,0.2,0.19,0.1,0.37,0.21,0,0,0.16,0,0.16,0.09,0.1,0,0.09,0.34,0.17,0.34,0.34,0,45.14,82.43,93.8,95.28,42.55,1.61,0.29,0.28,0.99,0.49,0.22,0.79,0.25,"B","11","Control","Blood" +"7-Blood",14.25,19.05,8.29,81.44,86.3,82.33,4.48,17.3,1.43,8.11,6.76,5.1,32.47,12.04,20.71,56.57,47.43,45.24,25.7,89.92,82.13,88.75,75.79,95.22,97.01,93.66,93.11,91.56,84.03,81.01,95.43,72.73,95.03,93.91,93.37,92.79,95.03,94.74,90.95,96.19,90.51,96.22,80.07,64.23,48.9,85.5,80.26,97.25,95.71,93.88,0.38,0.28,0.29,0.43,0.2,0.41,0.4,0.42,0.2,0.35,0.34,0.5,0.35,0.54,0.52,0.41,0.46,75.53,68.23,86.79,68.17,89.19,67.61,98.64,0.14,0.15,0.09,0.61,0.31,0.32,0.14,0.28,0.16,0.18,0.67,0.38,0.29,0.16,0.16,0.19,0.3,0.12,0.23,0.13,0.21,0.26,0.36,0.27,0.11,0.28,0.24,0.24,0.53,0.19,0.4,0.17,0.2,0.31,0.25,0.13,0.45,72.06,0,0.27,0.1,0.26,0.26,0.41,0.11,0.16,0.29,0.28,0.26,0.41,0.17,0.4,0.11,0.12,0.13,0.29,0.11,0,0.18,0.17,0,0.16,0.17,0.05,0.36,0.1,0.3,0.1,0.25,1.17,59.36,89.94,96.22,95.24,53.3,6.23,0.45,0.34,2.04,0.29,0.41,0.56,1.21,"B","9","ELS","Blood" +"71-Blood",14.89,16.34,10,77.87,83.39,74.53,3.46,25.34,2.46,9.61,8.27,7.09,32.74,13.6,20.09,56.52,38.12,42.13,30.01,88.9,81.12,83.21,77.46,94.98,94.25,96.42,87.04,88.71,80.08,77.22,94.9,63.08,96.1,94.04,95.55,94.24,93.63,93.66,84.86,86.71,92,93.18,62.04,64.28,53.04,80.11,83.05,93.34,95.46,91.23,0.25,0.39,0.19,0.31,0.2,0.3,0.26,0.22,0.17,0.28,0.22,0.26,0.18,0.32,0.18,0.28,0.16,74.39,51.42,77.65,63.22,80.99,66.99,89.76,0.25,0.18,1.09,0.21,0.44,1.07,0.21,1.07,1.42,0.65,0.75,0.92,0.88,0.7,0.82,0.86,0.82,0.68,0.82,0.25,0.21,0.35,0.18,0.13,0.25,0.26,0.55,0.33,0.33,0.26,0.44,0.19,0.3,0.22,0.38,0.1,0.4,73.04,0.05,0.87,0.2,0.55,0.1,0.25,0.15,0.31,0.28,0.16,0.26,0.45,0.17,0.33,0.21,0.36,0.2,0.12,0.11,0.11,0.09,0,0.1,0.11,0.17,0,0.1,0.35,0.25,0.3,0.15,0.25,47.99,82.57,94.06,93.74,44.25,3.9,0.44,0.47,1.55,0.39,0.57,1.52,0.76,"A","11","ELS","Blood" +"73-Blood",13.33,15.08,10.46,70.35,77.47,74.73,4.7,20.92,3.19,11.64,8.25,4.71,32.48,12.35,18.89,56.99,45.19,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,76.58,71.91,93.25,70.2,95.24,89.68,94.65,94.88,91.93,82.51,92.14,87.52,89.93,91.59,71.97,74.04,60.42,75.87,75.09,96.16,94.92,82.69,0.16,0.43,0.35,0.37,0.16,0.21,0.35,0.34,0.39,0.36,0.25,0.32,0.29,0.35,0.24,0.19,0.25,86.33,53.42,77.11,42.96,79.39,60.71,87.12,0.23,0.26,0.17,0.25,0.49,0.29,0.25,0.49,0.2,0.29,0.28,0.31,0.16,0.22,0.18,0.5,0.18,0.17,0.17,0.19,0.35,0.27,0.26,0.21,0.18,0.22,0.26,0.32,0.18,0.35,0.21,0.32,0.21,0.24,0.29,0.16,0.38,71.44,0.48,0.23,0.63,0.16,0.13,0.32,0.29,0.23,0.35,0.34,0.03,0.36,0.32,0.17,0.44,0.07,0.31,0.42,0.14,0.07,0.11,0.05,0,0.17,0.07,0.17,0.09,0.19,0.28,0.16,0.25,0.09,45.58,84.98,94.93,95.37,44.19,2.09,0.23,0.47,1.51,NA,NA,NA,NA,"C","10","ELS","Blood" +"75-Blood",14.21,13.88,6.94,67.96,81.01,73,6.96,17.87,3.54,11.97,7.41,5.75,30.27,8.12,17.57,53.3,44.14,42.13,29.92,85.13,77.57,77.24,63.05,94.88,92.9,93.33,88.87,91.48,75.24,72.11,92.5,66.88,97.43,92.42,92.64,92.97,93.83,88.38,94.15,88.86,89.84,93.61,82.37,76.51,63.93,73.83,71.78,96.78,95.67,84.15,0.19,0.3,0.15,0.29,0.4,0.27,0.41,0.33,0.34,0.25,0.33,0.29,0.24,0.25,0.29,0.3,0.21,67.77,50.36,71.02,50.37,78.63,61.48,88.39,0.23,0.23,0.15,0.19,0.2,0.12,0.08,0.16,0.28,0.17,0.12,0.16,0.12,0.08,0.23,0.12,0.08,0.2,0.27,0.39,0.38,0.23,0.22,0.12,0.23,0.13,0.42,0.26,0.14,0.42,0.49,0.09,0.41,0.44,0.22,0.24,0.32,73.43,0.1,0.33,0.13,0.28,0.16,0.22,0.13,0.16,0.07,0.27,0.42,0.22,0.14,0.1,0.27,0.23,0.08,0,0.21,0,0,0.05,0,0.2,0.03,0.16,0.19,0.59,0.38,0.25,0.47,0.12,45.12,82.43,95.16,94.01,42.63,2.85,0.35,0.62,0.88,0.19,0.5,0.57,0.91,"A","10","ELS","Blood" 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+"47-HIP",77.15,81.16,87.98,82.01,74.89,57.63,7.52,12.15,9.24,24.46,9.12,15.41,69.09,66.76,76.94,85.21,83.14,72.57,50.26,69.51,61.66,49.93,21.33,84.13,91.65,86.34,92.05,90.35,85.41,81.66,82.77,42.73,86.12,65.01,88.58,91.88,88.77,52.66,80.25,78.85,79.01,93.89,73.52,85.02,85,96.86,79.99,88.99,85.66,63.54,0.19,0.35,0.27,0.28,0.19,0.24,0.48,0.5,0.22,0.24,0.61,0.36,0.2,0.3,0.43,0.32,0.29,61.47,13.05,33.53,13.35,40.45,22.01,47.4,0.31,0.21,0.1,0.29,0.29,0.18,0.12,0.26,0.22,0.14,0.28,0.2,0.14,0.14,0.23,0.12,0.16,0.2,0.21,0.16,0.36,0.2,0.34,0.29,0.31,0.32,0.52,0.63,0.41,1.01,3.01,0.43,1.18,3.1,1.98,2.35,1.7,71.19,0.3,0.31,0.26,0.26,0.05,0.26,0.18,0.09,0.29,0.33,0.31,0.1,0.09,0.1,0.18,0.15,0,0.34,0.1,0.18,0.08,0.07,0.08,0.32,0.15,0.18,0.09,0.21,0.17,0.21,0.3,0.25,65.81,59.04,92.41,95.32,16.17,1.78,0.27,2.3,0.23,0.42,1.85,4.01,3.23,"D","2","Control","HIP" 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4.0.4 (2021-02-15) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16 Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] ggpubr_0.4.0 broom_0.8.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 [10] ggplot2_3.3.6 tidyverse_1.3.1 \ No newline at end of file