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diff --git a/runs/Figure_assembly/Figure_9_letter.pdf b/runs/Figure_assembly/Figure_9_letter.pdf
index b1bf04ae52c22a73f3bedc97d26da33904089ae7..0f3245c2e713715a365c07fe748a3fb392e9ef81 100644
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diff --git a/runs/Figure_assembly/Image.RData b/runs/Figure_assembly/Image.RData
index 80af522d6b9ca3bcc3defe867b86fde5ad774a39..4fd19bdb9c185509ada879a01776940d04837089 100644
--- a/runs/Figure_assembly/Image.RData
+++ b/runs/Figure_assembly/Image.RData
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:102896af09a1020810fa2273fc709cf30cdce9bf588e8e83c3cc98b2fd223460
-size 254738232
+oid sha256:e125d305a6cbbb1e957f6ea2d93cb4c29ccbc859b8e49a4145fc552c0e49dba8
+size 256073330
diff --git a/runs/Figure_assembly/Supplemental_Figure_S2.jpg b/runs/Figure_assembly/Supplemental_Figure_S2.jpg
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diff --git a/runs/Figure_assembly/Supplemental_Figure_S2.pdf b/runs/Figure_assembly/Supplemental_Figure_S2.pdf
index 000383d9be2c31a1a63221f3caa3e3f40179d06e..a83fa02ebee2d255d750fbd888258a90a8a3b2e7 100644
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diff --git a/runs/Figure_assembly/Supplemental_Figure_S9.jpg b/runs/Figure_assembly/Supplemental_Figure_S9.jpg
index db0d640e3e74550f9f3d5e2a6e9b7746b52bef0a..7c44da72ee3e01467fad3dbfa0f99e5c8db72bc1 100644
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diff --git a/runs/Figure_assembly/Supplemental_Figure_S9.pdf b/runs/Figure_assembly/Supplemental_Figure_S9.pdf
index 7ea29ea2fa85952db09a263877103eff9661532d..74f8347e912ed2f797c333e4e808761e9648af23 100644
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diff --git a/runs/Figure_assembly/executing.R b/runs/Figure_assembly/executing.R
index 12b574a4526afb072d31a472d3516abc9fccda06..e26d4f4589a6949536b569056ccd73e75379306f 100644
--- a/runs/Figure_assembly/executing.R
+++ b/runs/Figure_assembly/executing.R
@@ -4,7 +4,7 @@ library(cowplot)
library(svglite)
library(magick)
library(ggplotify)
-library(extrafont)
+#library(extrafont)
library(ggtext)
library(tidyverse)
library(here)
@@ -29,11 +29,11 @@ multi <- here("runs/multiomics_integration/")
study17 <- here("studies/canal-1_2017_GH/resources/")
#temp hard code
-rds_source <- here("../Manuscript_CANA1_2022/Figures/RDS_files")
+rds_source <- here("C:/Nextcloud_Data/MPI_Data/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files")
# Common themes -----------------------------------------------------------
-arial <- theme(text = element_text(family = "Arial"))
+#arial <- theme(text = element_text(family = "Arial"))
bot_leg <- theme(legend.position = "bottom")
rem_leg <- theme(legend.position = "")
com_theme <- theme(text = element_text(size = 8))
@@ -220,7 +220,7 @@ ggsave(here(out, "Figure_2_letter.pdf"), f2_letter, width = 21.59, height = 27.9
f3 <- read_rds(here(rna_fig, "leaf_glutaredoxins.RDS"))
f3
-ggsave(here(out, "Figure_3.pdf"), f4a, dpi = 600, width = 16.8, height = 10, units = "cm")
+ggsave(here(out, "Figure_3.pdf"), f3, dpi = 600, width = 16.8, height = 10, units = "cm")
header <- ggplot() + geom_text(aes(x = 0, y = 0 ,label = "Figure 3"), size = 20) + theme_void()
header
@@ -411,7 +411,7 @@ plot(ps_back)
ps_combined <- image_mosaic(c(ps, ps_back))
f7 <- ggplotify::as.ggplot(ps_combined)
-
+f7
ggsave(filename = here(out, "Figure_7.pdf"),
plot = f7,
dpi = 600,
@@ -468,7 +468,7 @@ f9 <- image_read_svg(here(multi, "SBGN_view_Multilevel_Metabolism.sbgn.svg"),
f9
f9 <- ggplotify::as.ggplot(f9)
-
+f9
ggsave(filename = here(out, "Figure_9.pdf"),
plot = f9,
dpi = 600,
@@ -535,8 +535,7 @@ f11bc_leg <- get_legend(f11c + bot_leg)
#plot_comb(f11a, f11b)
f11ab <- plot_grid(f11a, f11b, rel_widths = c(1,1),
labels = "AUTO",
- label_size = 8,
- label_fontfamily = "Arial")
+ label_size = 8)
f11ab
@@ -545,8 +544,7 @@ f11_c_with_leg
f11 <- plot_grid(f11ab, f11_c_with_leg, rel_heights = c(8.4, 11.5),
nrow = 2,
labels = c("", "C"),
- label_size = 8,
- label_fontfamily = "Arial")
+ label_size = 8)
f11
diff --git a/runs/Met_whole/Heatmap_all.jpg b/runs/Met_whole/Heatmap_all.jpg
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diff --git a/runs/Met_whole/asparagine_leaves.RDS b/runs/Met_whole/asparagine_leaves.RDS
new file mode 100644
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diff --git a/runs/Met_whole/ma_concentration_table.csv b/runs/Met_whole/ma_concentration_table.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e4ef055038f31f18ff3ec7f4d6cc9cfe125b69cd
--- /dev/null
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diff --git a/runs/apolar_LC_analysis/executing.R b/runs/apolar_LC_analysis/executing.R
new file mode 100644
index 0000000000000000000000000000000000000000..a28d89fc35609de78aa8b9eeb298b2c4d27728dd
--- /dev/null
+++ b/runs/apolar_LC_analysis/executing.R
@@ -0,0 +1,820 @@
+# Library -----------------------------------------------------------------
+#library(openxlsx)
+#library(car)
+#library(pheatmap)
+#library(broom)
+#library(ggpubr)
+#library(viridisLite)
+
+library(imputeLCMD)
+library(openxlsx)
+library(readxl)
+library(car)
+library(broom)
+library(pheatmap)
+library(ggpubr)
+library(tidyverse)
+library(here)
+
+# Directories -------------------------------------------------------------
+
+study_17 <- here("studies/canal-1_2017_GH/")
+study_18 <- here("studies/canal-1_2018_GH/")
+study_19 <- here("studies/canal-1_2019_GH/")
+
+assay <- here("assays/canal-1_apolar_LC_MS_2021/")
+dat_in <- here("runs/apolar_LC_library_selection/")
+
+setwd(here())
+
+out <- here("runs/apolar_LC_analysis")
+
+if (file.exists(out)) {
+ cat("The folder already exists")
+} else {
+ dir.create(out)
+}
+
+# Sample loading ----------------------------------------------------------
+
+isa_study_17 <- read_xlsx(here(study_17, "isa.study.xlsx"))
+isa_study_18 <- read_xlsx(here(study_18, "isa.study.xlsx"))
+isa_study_19 <- read_xlsx(here(study_19, "isa.study.xlsx"))
+
+isa_ext <- read_xlsx(here(assay, "isa.assay.xlsx"), sheet = 1)
+isa_chrom <- read_xlsx(here(assay, "isa.assay.xlsx"), sheet = 2)
+isa_ms <- read_xlsx(here(assay, "isa.assay.xlsx"), sheet = 3)
+
+isa_study_17 <- isa_study_17 %>%
+ mutate(`Source Name` = as.character(`Source Name`))
+isa_study_18 <- isa_study_18 %>%
+ mutate(`Source Name` = as.character(`Source Name`),
+ `Characteristic [plantline]` = as.character(`Characteristic [plantline]`))
+isa_study_19 <- isa_study_19 %>%
+ mutate(`Source Name` = as.character(`Source Name`),
+ `Characteristic [plantline]` = as.character(`Characteristic [plantline]`))
+
+isa_study <- bind_rows(isa_study_17, isa_study_18, isa_study_19)
+
+isa_study_tidy <- isa_study %>%
+ dplyr::select(source_name_study = `Source Name`,
+ tissue_group = `Characteristic [organism part]`,
+ year = `Characteristic [year]`,
+ leaf_col = `Characteristic [leaf color]`,
+ genotype = `Characteristic [genotype]`,
+ treatment = `Parameter [treatment]`,
+ sample_name_study = `Sample Name`)
+
+isa_ext_tidy <- isa_ext %>%
+ dplyr::select(source_name_ext = `Source Name`,
+ ext_year = `Characteristic [extraction year]`,
+ sample_weight = `Parameter [Sample Weight]`,
+ sample_name_ext = `Sample Name`)
+
+isa_chrom_tidy <- isa_chrom %>%
+ dplyr::select(source_name_chrom = `Source Name`,
+ #class = `Parameter [MS sample type]`,
+ batch = `Parameter [sample batch]`,
+ #daily_num = `Parameter [daily number]`,
+ running_num = `Parameter [Number]`,
+ #split = `Parameter [open split]`,
+ sample_name_chrom = `Sample Name`) %>%
+ group_by(batch) %>%
+ mutate(daily_num = row_number(),
+ class = "sample" # to be removed when in isa.assay
+ ) %>%
+ ungroup()
+
+isa_ms_tidy <- isa_ms %>%
+ dplyr::select(source_name_ms = `Source Name`,
+ Filename = `Raw Data File`)
+
+isa_tidy <- isa_study_tidy %>%
+ full_join(isa_ext_tidy, by = c("sample_name_study" = "source_name_ext")) %>%
+ full_join(isa_chrom_tidy, by = c("sample_name_ext" = "source_name_chrom")) %>%
+ full_join(isa_ms_tidy, by = c("sample_name_chrom" = "source_name_ms"))%>%
+ mutate(genotype_alt = if_else(str_detect(genotype, "M82"),
+ "WT",
+ "MU"),
+ tissue = if_else(str_detect(tissue_group, "leaf"),
+ str_c(leaf_col, tissue_group),
+ tissue_group),
+ group = str_c(genotype_alt, tissue, sep = "_")) %>%
+ arrange(batch, sample_name_chrom)
+
+# Data loading ------------------------------------------------------------
+
+area_18_19 <- read_delim(file = here(dat_in, "CANA1_selected_lipids.txt"),
+ delim = "\t") %>%
+ rename(met = Name,
+ mz_mean_input = mz_mean,
+ RT_mean_input = RT_mean,
+ mz_mean = mz_mean_current,
+ RT_mean = RT_mean_current) %>%
+ group_by(met) %>%
+ mutate(rt_diff = RT_mean - RT_mean_input,
+ rank_rt = rank(abs(rt_diff))) %>%
+ ungroup() %>%
+ filter(rank_rt == 1) %>%
+ group_by(Compound_Name) %>%
+ mutate(peak_no = rank(RT_mean),
+ Compound_Name = if_else(duplicated(Compound_Name),
+ str_c(Compound_Name, "peak", peak_no, sep = "_"),
+ Compound_Name))
+
+metabolitedata_18_19 = area_18_19 %>%
+ select(met, mz_mean,RT_mean, Compound_Name, Compound_Class)
+
+trtmnts <- isa_tidy %>%
+ select(source_name_study, treatment)
+
+
+# Data combination --------------------------------------------------------
+
+lipid_classes <- area_18_19 %>%
+ distinct(Compound_Class, Compound_Name, Peak.ID, met)
+
+#isa_tidy <- isa_tidy %>%
+# bind_rows(sampledata_17_tidy)
+
+samp_vars <- c("year", "tissue", "treatment", "genotype",# "sample_ID",
+ "sample_name_study", "machine_num_lipids", "aliquot_ID",
+ "sample_weight", "class", "ext_year")
+
+sampledata_18_19_tidy <- isa_tidy %>%
+ #select(year, tissue, genotype,
+ # aliquot_ID = source_name_study,
+ # sample_weight,
+ # machine_num_lipids = sample_name_chrom) %>%
+ #left_join(trtmnts) %>%
+ mutate(genotype = as_factor(genotype),
+ tissue = as_factor(tissue),
+ treatment = as_factor(treatment),
+ aliquot_ID = source_name_study,
+ machine_num_lipids = sample_name_chrom)
+
+area_long <- area_18_19 %>%
+ pivot_longer(starts_with("X"),
+ names_to = "machine_num_lipids",
+ names_prefix = "X",
+ values_to = "area") %>%
+ left_join(sampledata_18_19_tidy) %>%
+ filter(year != "2020", !is.na(genotype), !is.na(treatment))
+
+means <- area_long %>%
+ mutate(fw_norm = area/sample_weight) %>%
+ group_by(Peak.ID, met, tissue, treatment, genotype) %>%
+ summarise(mean = mean(fw_norm)) %>%
+ group_by(met, Peak.ID, treatment) %>%
+ mutate(total_norm = mean/mean(mean),
+ log_norm = log2(total_norm),
+ log_norm = if_else(is.na(log_norm)| is.infinite(log_norm), 0, log_norm),
+ log_norm_level = (log_norm - mean(log_norm))/(max(log_norm)-min(log_norm)),
+ log_norm_level = if_else(is.na(log_norm_level), 0, log_norm_level)) %>%
+ ungroup()
+
+pigments <- area_long %>%
+ filter(Compound_Class == "Pigment")
+
+#write_csv(pigments, "H:/2. CANA1, SCO2/CANA1_pigment_analysis/pigments_apolar_LC.csv")
+
+# Imputation --------------------------------------------------------------
+
+set_back_NA <- function(x){
+ for (i in seq_along(x)) {
+ if (x[[i]] == 0) {
+ x[[i]] <- NA
+ } else {
+ x[[i]] <- x[[i]]
+ }
+ }
+ x
+}
+
+features_na <- area_long %>%
+ group_by(met) %>%
+ mutate(area = set_back_NA(area)) %>%
+ ungroup()
+
+missingness <- features_na %>%
+ group_by(met, tissue, year) %>%
+ summarise(na = sum(is.na(area)),
+ n = n()) %>%
+ mutate(percent_na = na/n*100) %>%
+ ungroup()
+
+features <- features_na %>%
+ left_join(missingness) %>%
+ group_by(met) %>%
+ #mutate(area = ifelse(percent_na >= 0 & is.na(area), rnorm(n = 1, mean = 0.5*min(area, na.rm = T)), area)) %>%
+ ungroup()
+
+#miss_for_imp <- features %>%
+# pivot_wider(names_from = met,
+# values_from = area,
+# id_cols = c(exp, machine_num_lip, met, tissue, treatment)) %>%
+# select(exp, machine_num_lip, tissue, treatment, matches("Cluster_")) %>%
+# group_by(exp, tissue, treatment) %>%
+# nest() %>%
+# mutate(new = map(.x = data, .f = as.data.frame),
+# new = map(.x = new, .f = ~`row.names<-`(.x, .x$machine_num_lip)),
+# new = map(.x = new, .f = ~select(.x, !starts_with("machine"))),
+# new = map(.x = new, .f = as.matrix),
+# imp = map_dfc(.x = new, .f = impute.QRILC)[[1]])
+
+#imp_out_loop <- vector(mode = "list", length = length(miss_for_imp$new))
+
+#for (j in seq_along(miss_for_imp$new)) {
+# imp_out_loop[[j]] <- impute.QRILC(miss_for_imp$new[[j]])[[1]]
+#}
+
+#can currently not solve per tissue, treatment and exp
+#proceed with qrilc on all
+
+imp <- features_na %>%
+ pivot_wider(names_from = met,
+ values_from = area,
+ id_cols = c(machine_num_lipids, tissue, treatment)) %>%
+ select(machine_num_lipids, tissue, treatment, matches("Cluster_")) %>%
+ as.data.frame()
+
+row.names(imp) <- imp$machine_num_lipids
+imp <- imp %>%
+ select(-tissue, -treatment, -machine_num_lipids) %>%
+ as.matrix()
+
+imp <- impute.QRILC(imp)
+
+
+features <- imp[[1]] %>%
+ as_tibble(rownames = "machine_num_lipids") %>%
+ pivot_longer(cols = matches("Cluster_"),
+ names_to = "met",
+ values_to = "imp") %>%
+ left_join(features_na)
+
+adjust_imp <- features %>%
+ group_by(met) %>%
+ summarise(offset = if_else (any(imp <0), 1.001*abs(min(imp)),0))
+
+
+features_filtered <- features %>%
+ left_join(missingness) %>%
+ #filter(percent_na <= 60)
+ left_join(adjust_imp) %>%
+ mutate(imp = imp + offset) %>%
+ select(-offset)
+
+# Significance analysis ---------------------------------------------------
+
+data_2018_19 <- features_filtered %>%
+ mutate(fw_norm = imp/sample_weight) %>%
+ pivot_wider(id_cols = c(tissue, treatment, genotype, machine_num_lipids, aliquot_ID),
+ names_from = met,
+ values_from = fw_norm) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_"))) %>%
+ ungroup()
+
+
+data_2018_19_numeric <- data_2018_19 %>%
+ select(starts_with("Cluster_"))
+
+levene_2018_19 <- map_dfc(.x = data_2018_19_numeric, .f = ~leveneTest(.x ~ data_2018_19$tissue * data_2018_19$genotype * data_2018_19$treatment))
+kruskal_2018_19 <- map_dfc(.x = data_2018_19_numeric, .f = ~kruskal.test(.x ~ data_2018_19$group)$p.value)
+wilcox_2018_19 <- map(.x = data_2018_19_numeric, .f = ~pairwise.wilcox.test(.x, data_2018_19$group, p.adjust.method = "none"))
+
+all_pair_wilcox_2018_19 <- wilcox_2018_19 %>%
+ map(.f = tidy) %>%
+ map2(.y = names(wilcox_2018_19), .f = ~.x %>% mutate(var = .y)) %>%
+ purrr::reduce(bind_rows)
+
+sig_2018_19 <- all_pair_wilcox_2018_19 %>%
+ separate(col = group1, into = c("tissue1", "treatment1", "genotype1"), sep = "_") %>%
+ separate(col = group2, into = c("tissue2", "treatment2", "genotype2"), sep = "_") %>%
+ mutate(tissue_group1 = if_else(str_detect(tissue1, "leaf"), "leaf", tissue1)) %>%
+ mutate(tissue_group2 = if_else(str_detect(tissue2, "leaf"), "leaf", tissue2)) %>%
+ filter(tissue_group1 == tissue_group2, treatment1 == treatment2) %>%
+ group_by(tissue_group1, tissue_group2, treatment1, treatment2, var) %>%
+ mutate(p_adj = p.adjust(p.value, method = "fdr")) %>%
+ filter(p_adj <= 0.05,
+ genotype1 == "M82" | genotype2 == "M82") %>%
+ ungroup()
+
+
+sig_2018_19_1 <- sig_2018_19 %>%
+ select(tissue = tissue1,
+ genotype = genotype1,
+ treatment = treatment1,
+ tissue_group = tissue_group1, p_adj, var, p.value)
+
+sig_2018_19_2 <- sig_2018_19 %>%
+ select(tissue = tissue2,
+ genotype = genotype2,
+ treatment = treatment2,
+ tissue_group = tissue_group2, p_adj, var, p.value)
+
+
+sig_2018_19_ann <- sig_2018_19_1 %>%
+ bind_rows(sig_2018_19_2) %>%
+ filter(genotype != "M82") %>%
+ mutate(signif = "*") %>%
+ rename(met = var) %>%
+ mutate(genotype = as_factor(genotype),
+ tissue = as_factor(tissue),
+ treatment = as_factor(treatment))
+
+sig_mets_2018_19 <- sig_2018_19_ann %>%
+ distinct(met) %>%
+ mutate(sig = T)
+
+
+norm_2018_19 <- data_2018_19 %>%
+ pivot_longer(starts_with("Cluster_"),
+ names_to = "met",
+ values_to = "fw_norm") %>%
+ left_join(metabolitedata_18_19) %>%
+ group_by(tissue, treatment, genotype, Compound_Name, met, aliquot_ID) %>%
+ summarise(tec_rep_norm = mean(fw_norm)) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_"))) %>%
+ ungroup() %>%
+ mutate(tissue_group = if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue)))
+
+norm_2018_19_means <- norm_2018_19%>%
+ left_join(sig_mets_2018_19) %>%
+ filter(sig == T) %>%
+ group_by(tissue, treatment, genotype, Compound_Name, met) %>%
+ summarise(mean = mean(tec_rep_norm),
+ sd = sd(tec_rep_norm),
+ n = n(),
+ se = sd/sqrt(n)) %>%
+ ungroup() %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue)))
+
+
+# Heatmap -----------------------------------------------------------------
+
+heat.lip_2018_19 <- norm_2018_19_means %>%
+ filter(tissue != "flowers") %>%
+ group_by(Compound_Name, met) %>%
+ mutate(total_norm = mean/mean(mean),
+ log_norm = log2(total_norm),
+ log_norm = if_else(is.infinite(log_norm), 0, log_norm),
+ log_norm_level = (log_norm - mean(log_norm))/(max(log_norm)-min(log_norm))) %>%
+ ungroup() %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))) %>%
+ pivot_wider(id_cols = c(Compound_Name, met),
+ names_from = group,
+ values_from = log_norm_level) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+
+rownames(heat.lip_2018_19) <- heat.lip_2018_19$met
+
+mat.heat.lip_2018_19 <- heat.lip_2018_19 %>%
+ select(contains("stem"), contains("leaf"), contains("root"), contains("flower"), contains("fruit")) %>% as.matrix()
+
+
+
+annotation_row_2018_19 <- heat.lip_2018_19 %>%
+ select(Compound_Class)
+
+rownames(annotation_row_2018_19) <- heat.lip_2018_19$met
+
+annotation_col_2018_19 <- sampledata_18_19_tidy %>%
+ filter(year == 2018| year == 2019, tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = as_factor(if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))),
+ genotype_group = if_else(str_detect(genotype, "^M82"), "M82", "CANA1")) %>%
+ as.data.frame()
+
+rownames(annotation_col_2018_19) <- annotation_col_2018_19$group
+
+annotation_col_2018_19 <- annotation_col_2018_19 %>%
+ select(-group, -treatment, -tissue, -genotype, genotype_group, tissue_group, treatment)
+
+heat.lip_2018_19_signif <- norm_2018_19_means %>%
+ filter(tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype, Compound_Name, met) %>%
+ left_join(sig_2018_19_ann) %>%
+ left_join(sig_mets_2018_19) %>%
+ filter(sig == T) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ signif = if_else(is.na(signif), "","X")) %>%
+ pivot_wider(id_cols = c(met, Compound_Name),
+ names_from = group,
+ values_from = signif) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+heat_cols_2018_19 <- colnames(mat.heat.lip_2018_19)
+
+rownames(heat.lip_2018_19_signif) <- heat.lip_2018_19_signif$met
+
+mat.heat.lip_2018_19_signif <- heat.lip_2018_19_signif %>%
+ select(all_of(heat_cols_2018_19)) %>% as.matrix()
+
+ann_colors_2018_19 = list(
+ tissue_group = c(fruit = "red",leaf = "darkgreen",root = "grey", stem = "lightgreen", flower = "yellow"),
+ genotype_group = c(M82 = "blue", CANA1 = "red"),
+ treatment = c(control = "purple", drought = "orange"),
+ Compound_Class = c("Triacylglyceride"= "#000000",
+ "lyso Phospholipid" = "#E69F00",
+ "Lyso-Monogalactosyldiacylglycerol" = "#56B4E9",
+ "Lyso-Digalactosyl-Diacylglycerol" = "#A25B34",
+ "Diacylglycerol" = "#009E73",
+ "Phospholipid" = "#F0E442",
+ "Phosphatidylcholine" = "#0072B2",
+ "Sphingolipid" = "#D55E00",
+ "Monogalactosyldiacylglycerol" = "#CC79A7",
+ "Digalactosyl-Diacylglycerol" = "#9487FD",
+ "Phosphatidylethanolamine" = "#DDB73B",
+ "Pigment" = "#F31886"
+ )
+
+)
+
+pheatmap.lip <- pheatmap(mat.heat.lip_2018_19,
+ #color = plasma(14),
+ cellwidth = 16,
+ cellheight = 4,
+ #breaks = c(-6.5,-5.5,-4.5,-3.5,-2.5,-1.5, -0.5 ,0.5,1.5,2.5,3.5,4.5,5.5,6.5),
+ cluster_rows = T,
+ cluster_cols = T,
+ annotation_names_row = F,
+ show_rownames = F,
+ annotation_row = annotation_row_2018_19,
+ annotation_col = annotation_col_2018_19,
+ display_numbers = mat.heat.lip_2018_19_signif,
+ number_color = "black",
+ fontsize_number = 6,
+ annotation_colors = ann_colors_2018_19,
+ filename = here(out,
+ "CANA1_lip_2018_19_heatmap_rel_all.jpg")
+)
+
+# Heatmap scaled per tissue-----------------------------------------------------------------
+
+heat.lip_2018_19 <- norm_2018_19_means %>%
+ filter(tissue != "flowers") %>%
+ group_by(treatment, tissue_group, Compound_Name, met) %>%
+ mutate(total_norm = mean/mean(mean),
+ log_norm = log2(total_norm),
+ log_norm = if_else(is.infinite(log_norm), 0, log_norm),
+ log_norm_level = (log_norm - mean(log_norm))/(max(log_norm)-min(log_norm))) %>%
+ ungroup() %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))) %>%
+ pivot_wider(id_cols = c(Compound_Name, met),
+ names_from = group,
+ values_from = log_norm_level) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+rownames(heat.lip_2018_19) <- heat.lip_2018_19$met
+
+mat.heat.lip_2018_19 <- heat.lip_2018_19 %>%
+ select(contains("stems"), contains("leaf"), contains("roots"), contains("flowers"), contains("fruits")) %>% as.matrix()
+
+annotation_row_2018_19 <- heat.lip_2018_19 %>%
+ select(Compound_Class)
+
+rownames(annotation_row_2018_19) <- heat.lip_2018_19$met
+
+annotation_col_2018_19 <- sampledata_18_19_tidy %>%
+ filter(year == 2018| year == 2019, tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = as_factor(if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))),
+ genotype_group = if_else(str_detect(genotype, "^M82"), "M82", "CANA1")) %>%
+ as.data.frame()
+
+rownames(annotation_col_2018_19) <- annotation_col_2018_19$group
+
+annotation_col_2018_19 <- annotation_col_2018_19 %>%
+ select(-group, -treatment, -tissue, -genotype, genotype_group, tissue_group, treatment)
+
+heat.lip_2018_19_signif <- norm_2018_19_means %>%
+ filter(tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype, Compound_Name, met) %>%
+ left_join(sig_2018_19_ann) %>%
+ left_join(sig_mets_2018_19) %>%
+ filter(sig == T) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ signif = if_else(is.na(signif), "","X")) %>%
+ pivot_wider(id_cols = c(met, Compound_Name),
+ names_from = group,
+ values_from = signif) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+heat_cols_2018_19 <- colnames(mat.heat.lip_2018_19)
+
+rownames(heat.lip_2018_19_signif) <- heat.lip_2018_19_signif$met
+
+mat.heat.lip_2018_19_signif <- heat.lip_2018_19_signif %>%
+ select(all_of(heat_cols_2018_19)) %>% as.matrix()
+
+ann_colors_2018_19 = list(
+ tissue_group = c(fruits = "red",leaf = "darkgreen",roots = "grey", stems = "lightgreen", flowers = "yellow"),
+ genotype_group = c(M82 = "blue", CANA1 = "red"),
+ treatment = c(control = "purple", drought = "orange"),
+ Compound_Class = c("Triacylglyceride"= "#000000",
+ "lyso Phospholipid" = "#E69F00",
+ "Lyso-Monogalactosyldiacylglycerol" = "#56B4E9",
+ "Lyso-Digalactosyl-Diacylglycerol" = "#A25B34",
+ "Diacylglycerol" = "#009E73",
+ "Phospholipid" = "#F0E442",
+ "Phosphatidylcholine" = "#0072B2",
+ "Sphingolipid" = "#D55E00",
+ "Monogalactosyldiacylglycerol" = "#CC79A7",
+ "Digalactosyl-Diacylglycerol" = "#9487FD",
+ "Phosphatidylethanolamine" = "#DDB73B",
+ "Pigment" = "#F31886"
+ )
+)
+
+pheatmap.lip <- pheatmap(mat.heat.lip_2018_19,
+ #color = plasma(14),
+ cellwidth = 16,
+ cellheight = 4,
+ #breaks = c(-6.5,-5.5,-4.5,-3.5,-2.5,-1.5, -0.5 ,0.5,1.5,2.5,3.5,4.5,5.5,6.5),
+ cluster_rows = T,
+ cluster_cols = T,
+ annotation_names_row = F,
+ show_rownames = F,
+ annotation_row = annotation_row_2018_19,
+ annotation_col = annotation_col_2018_19,
+ display_numbers = mat.heat.lip_2018_19_signif,
+ number_color = "black",
+ fontsize_number = 6,
+ annotation_colors = ann_colors_2018_19,
+ filename = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),
+ "CANA1_lip_2018_19_heatmap_rel_tissue_gr.jpg",
+ sep = "_")
+)
+
+
+# Per metabolite comparisons 2018_19 ----------------------------------------------
+
+norm_2018_19_M82 <- norm_2018_19_means %>%
+ filter(genotype == "M82") %>%
+ select(tissue_group, treatment, met, M82_mean = mean)
+
+fc_2018_19 <- norm_2018_19 %>%
+ left_join(norm_2018_19_M82) %>%
+ mutate(fc = tec_rep_norm/M82_mean) %>%
+ group_by(tissue, treatment, genotype, met) %>%
+ summarise(mean_fc = mean(fc),
+ sd = sd(fc),
+ n = n()) %>%
+ mutate(se = sd/sqrt(n),
+ group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = as_factor(if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue)))) %>%
+ left_join(sig_2018_19_ann) %>%
+ group_by(met) %>%
+ filter(any(signif == "*"), all(is.finite(mean_fc)), tissue != "flowers") %>%
+ left_join(metabolitedata_18_19) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ ungroup()
+
+plotmets <- fc_2018_19 %>% distinct(met) %>% as_vector()
+plottissues <- fc_2018_19 %>% distinct(tissue_group) %>% as_vector()
+
+labelnames <- plotmets %>% as_tibble() %>%
+ rename(met = value) %>%
+ left_join(metabolitedata_18_19) %>% left_join(lipid_classes) %>%
+ select(Compound_Name) %>% as_vector()
+
+
+plot_out <- vector("list", length = length(plotmets))
+per_comp_y <- fc_2018_19 %>%
+ group_by(tissue_group, treatment, met) %>%
+ summarise(mean = max(mean_fc),
+ se = max(se))
+
+for(tiss in seq_along(plottissues)) {
+ for (meta in seq_along(plotmets)) {
+
+
+ sig_2018_19_bar <- sig_2018_19 %>%
+ rename(met = var) %>%
+ mutate(group1 = if_else(str_detect(tissue1, "white"), str_c(genotype1, " white"), genotype1),
+ group2 = if_else(str_detect(tissue2, "white"), str_c(genotype2, " white"), genotype2),
+ p.signif = if_else(p_adj <= 0.05, "*", "ns")) %>%
+ left_join(per_comp_y,by = c("met", "tissue_group1" = "tissue_group", "treatment1" = "treatment")) %>%
+ select(mean1 = mean, se1 = se, tissue1, treatment1, genotype1, group1,
+ tissue2, treatment2, genotype2, group2, met, p_adj, p.signif, tissue_group2) %>%
+ left_join(per_comp_y,by = c("met", "tissue_group2" = "tissue_group", "treatment2" = "treatment")) %>%
+ select(mean1, mean2 = mean, se1, se2 = se, tissue1, treatment1, genotype1, group1,
+ tissue2, treatment2, genotype2, group2, met, p_adj, p.signif, tissue_group2) %>%
+ group_by(group1, group2, tissue_group2, treatment2, met) %>%
+ mutate(y.position = max(mean1 + se1, mean2 + se2), treatment = treatment1,
+ y.position = y.position + 0.25*max(y.position)) %>%
+ ungroup() %>%
+ filter(met == plotmets[[meta]], tissue_group2 == plottissues[[tiss]])
+
+
+ plot_out[[tiss]][[meta]] <- fc_2018_19 %>%
+ filter(met == plotmets[[meta]], tissue_group == plottissues[[tiss]]) %>%
+ mutate(group = if_else(str_detect(tissue, "white"), str_c(genotype, " white"), as.character(genotype)),
+ group = as_factor(group),
+ group = fct_relevel(group, c("M82", "CANA1", "CANA1 white"))) %>%
+ ggplot(aes(x = group, y = mean_fc)) +
+ geom_col(position = "dodge", aes(fill = genotype), color = "black") +
+ geom_errorbar(aes(ymin = (mean_fc-se), ymax = (mean_fc + se)), position = position_dodge(0.9), width = 0.25, size = 0.75)+
+ stat_pvalue_manual(sig_2018_19_bar, label = "p.signif", y.position = "y.position",
+ step.increase = 0.07,
+ hide.ns = T) +
+ facet_grid(cols = vars(treatment)) +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1),
+ panel.background = element_rect(fill = "white"),
+ panel.border = element_rect(color = "black",fill = NA),
+ text = element_text(size = 8)) +
+ ggtitle(label = str_c(labelnames[[meta]], "in", plottissues[[tiss]], sep = " ")) +
+ scale_fill_manual(
+ values = c("black", "white"),
+ limits = c("CANA1", "M82"))
+ }
+
+}
+
+plot_out[[1]][[2]]
+
+pdf(file = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"CANA1_lipids_2018_19_col_plots.pdf"))
+
+for (tiss in seq_along(plottissues)) {
+ for (meta in seq_along(plotmets)) {
+ print(plot_out[[tiss]][[meta]])
+ }
+
+}
+
+dev.off()
+
+# Heatmap scaled Wildtype-----------------------------------------------------------------
+
+heat.lip_2018_19 <- fc_2018_19 %>%
+ filter(tissue != "flowers") %>%
+ group_by(Compound_Name, met) %>%
+ mutate(log_norm = log2(mean_fc),
+ log_norm = if_else(is.infinite(log_norm), 0, log_norm),
+ log_norm_level = (log_norm - mean(log_norm))/(max(log_norm)-min(log_norm))) %>%
+ ungroup() %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))) %>%
+ pivot_wider(id_cols = c(Compound_Name, met),
+ names_from = group,
+ values_from = log_norm) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+rownames(heat.lip_2018_19) <- heat.lip_2018_19$met
+
+mat.heat.lip_2018_19 <- heat.lip_2018_19 %>%
+ select(contains("stem"), contains("leaf"), contains("root"), contains("flower"), contains("fruit")) %>% as.matrix()
+
+annotation_row_2018_19 <- heat.lip_2018_19 %>%
+ select(Compound_Class)
+
+rownames(annotation_row_2018_19) <- heat.lip_2018_19$met
+
+annotation_col_2018_19 <- sampledata_18_19_tidy %>%
+ filter(year == 2018| year == 2019, tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ tissue_group = as_factor(if_else(str_detect(tissue, "leaf"), "leaf", as.character(tissue))),
+ genotype_group = if_else(str_detect(genotype, "^M82"), "M82", "CANA1")) %>%
+ as.data.frame()
+
+rownames(annotation_col_2018_19) <- annotation_col_2018_19$group
+
+annotation_col_2018_19 <- annotation_col_2018_19 %>%
+ select(-group, -treatment, -tissue, -genotype, genotype_group, tissue_group, treatment)
+
+heat.lip_2018_19_signif <- fc_2018_19 %>%
+ filter(tissue != "flowers") %>%
+ distinct(tissue, treatment, genotype, Compound_Name, met) %>%
+ left_join(sig_2018_19_ann) %>%
+ left_join(sig_mets_2018_19) %>%
+ filter(sig == T) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ signif = if_else(is.na(signif), "","X")) %>%
+ pivot_wider(id_cols = c( met, Compound_Name),
+ names_from = group,
+ values_from = signif) %>%
+ left_join(lipid_classes) %>%
+ arrange(Compound_Class, Compound_Name) %>%
+ as.data.frame()
+
+heat_cols_2018_19 <- colnames(mat.heat.lip_2018_19)
+
+rownames(heat.lip_2018_19_signif) <- heat.lip_2018_19_signif$met
+
+mat.heat.lip_2018_19_signif <- heat.lip_2018_19_signif %>%
+ select(all_of(heat_cols_2018_19)) %>% as.matrix()
+
+ann_colors_2018_19 = list(
+ tissue_group = c(fruit = "red",leaf = "darkgreen",root = "grey", stem = "lightgreen", flower = "yellow"),
+ genotype_group = c(M82 = "blue", CANA1 = "red"),
+ treatment = c(control = "purple", drought = "orange"),
+ Compound_Class = c("Triacylglyceride"= "#000000",
+ "lyso Phospholipid" = "#E69F00",
+ "Lyso-Monogalactosyldiacylglycerol" = "#56B4E9",
+ "Lyso-Digalactosyl-Diacylglycerol" = "#A25B34",
+ "Diacylglycerol" = "#009E73",
+ "Phospholipid" = "#F0E442",
+ "Phosphatidylcholine" = "#0072B2",
+ "Sphingolipid" = "#D55E00",
+ "Monogalactosyldiacylglycerol" = "#CC79A7",
+ "Digalactosyl-Diacylglycerol" = "#9487FD",
+ "Phosphatidylethanolamine" = "#DDB73B",
+ "Pigment" = "#F31886"
+ )
+)
+
+pheatmap.lip <- pheatmap(mat.heat.lip_2018_19,
+ color = colorRampPalette(c("#4474b4ff", "#add0e3ff", "#f0fad5ff", "#fba467ff", "#da3529ff"))(86),
+ cellwidth = 24,
+ cellheight = 4,
+ breaks = seq(-4.25, 4.25, 0.1),
+ cluster_rows = T,
+ cluster_cols = T,
+ annotation_names_row = F,
+ show_rownames = F,
+ annotation_row = annotation_row_2018_19,
+ annotation_col = annotation_col_2018_19,
+ display_numbers = mat.heat.lip_2018_19_signif,
+ number_color = "black",
+ fontsize_number = 6,
+ annotation_colors = ann_colors_2018_19,
+ filename = here(out,
+ "CANA1_lip_2018_19_heatmap_rel_tissue_wt.jpg"
+ )
+)
+
+# Additional formatting for cross metabolite comparison -------------------
+
+p_val <- sig_2018_19 %>%
+ rename(tissue = tissue1,
+ treatment = treatment1,
+ genotype = genotype1,
+ met = var) %>%
+ mutate(signif = if_else(p.value <= 0.05, "*", "ns"))
+
+M82_mean_tecrep_norm <- norm_2018_19 %>%
+ filter(genotype == "M82") %>%
+ group_by(tissue_group, treatment, Compound_Name, met) %>%
+ summarise(M82_mean = mean(tec_rep_norm)) %>%
+ ungroup()
+
+fc_1_ind_lip <- norm_2018_19 %>%
+ left_join(M82_mean_tecrep_norm) %>%
+ mutate(fc = tec_rep_norm/M82_mean)
+
+fc_1_lip <- fc_1_ind_lip %>%
+ group_by(tissue, treatment, Compound_Name, met, genotype) %>%
+ summarise(mean_fc = mean(fc),
+ n = n(),
+ sd = sd(fc)) %>%
+ ungroup() %>%
+ mutate(se = sd/sqrt((n))) %>%
+ left_join(p_val)
+
+samples_rna <- read_csv(here("runs/deseq2/sample_table.csv"))
+
+samples_to_match <- samples_rna %>%
+ mutate(sample_name_unique = str_c(Plant, tissue, sep = "_"))
+
+alllip_wide <- fc_1_ind_lip %>%
+ mutate(sample_name_unique = str_c(aliquot_ID, tissue, sep = "_")) %>%
+ select(sample_name_unique, met, fc) %>%
+ filter(sample_name_unique %in% samples_to_match$sample_name_unique) %>%
+ pivot_wider(names_from = met,
+ values_from = fc)
+
+write_csv(fc_1_lip, here(out, "mean_values_se_n.csv"))
+write_csv(fc_1_ind_lip, here(out, "individual_values.csv"))
+write_csv(p_val, here(out, "p_values.csv"))
+
+write_csv(metabolitedata_18_19, here(out, "metabolitedata_lipids.csv"))
+write_csv(alllip_wide, here(out, "Lip_wide.csv"))
+# Log used code ------------------------------------------------------------
+sessionInfo()
+file_name <