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differ diff --git a/studies/ext_network_ILs_lipid/README.md b/studies/ext_network_ILs_lipid/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/studies/ext_network_ILs_lipid/isa.study.xlsx b/studies/ext_network_ILs_lipid/isa.study.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..a709160fb6270f63eaa39ebf71885178864aabcc Binary files /dev/null and b/studies/ext_network_ILs_lipid/isa.study.xlsx differ diff --git a/studies/ext_network_ILs_lipid/protocols/.gitkeep b/studies/ext_network_ILs_lipid/protocols/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/studies/ext_network_ILs_lipid/resources/.gitkeep b/studies/ext_network_ILs_lipid/resources/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/studies/ext_network_ILs_lipid/resources/data_ready_for_network_190618.xlsx b/studies/ext_network_ILs_lipid/resources/data_ready_for_network_190618.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..c0ed559db980f4dc0474cf1e732c431bcb789733 Binary files /dev/null and b/studies/ext_network_ILs_lipid/resources/data_ready_for_network_190618.xlsx differ diff --git a/workflows/Canalization_comparison_ILs/Canalization_comparison_ILs.R b/workflows/Canalization_comparison_ILs/Canalization_comparison_ILs.R new file mode 100644 index 0000000000000000000000000000000000000000..0a0089919e8fac0540e80717eda5fe9cdbf81ca4 --- /dev/null +++ b/workflows/Canalization_comparison_ILs/Canalization_comparison_ILs.R @@ -0,0 +1,226 @@ +library(tidyverse) +library(ggtext) +library(ggrepel) +library(lme4) +library(openxlsx) +library(ggbeeswarm) +library(here) +#setwd("H:/1. cmQTL validation/Background/") +load(here("./../../1. cmQTL validation/Background/Supplemental_plcell_v29_11_2753_s1/TPC2017-LSB-00367R2_supplemental_file_2.gz")) +met_dat <- printnames_primary %>% as_tibble() + + +# Original snippet -------------------------------------------------------- +#e.g. Malate IL10-1 +dat <- data_primary_levene[[3345]] + +fit <- lmer(obs ~ gen + (1|env) + (1|gen:env), data = dat) +results <- Anova(fit) +fit.nointeraction <- lmer(obs ~ gen + (1|env), data = dat) + +results.compare <- anova(fit, fit.nointeraction) + +#write.csv(results.compare, +# str_c(str_replace_all(Sys.Date(),"^.{2}|-",""), +# "malate_IL10-1.csv", +# sep = "_")) + + +# Yield ------------------------------------------------------------------- + +cor_base <- readxl::read_xlsx(here("./../../14. Metabolite Correlation Network/data_ready_for_network_190618.xlsx")) + +yield_traits <- c("PW", "FW", "BX", "TY", "RED", "EA", "FN", "BM", "HI", "BY") + +potential_addition <- c("Fruit_length", "Fruit_width", "Fruit_L_W", "Fruit_peric_thick", "Fruit_W_Pt", "Fruit_locule", + "Seed_Length", "Seed_Width", "Seed_L_W", "Seed_Wt", "SN_fruit", "SW_fruit", "SN_plant", "SW_plant", "SN_fruit_unit", + "Anther_length", "Anther_width", "Anther_L_W", + "Ovary_width", "Ovary_length", "Ovary_L_W", "Style_width", "Style_length", "Style_L_W", + "inf", "flowers_inf", "flowers_plant") + +yield <- cor_base %>% + select(`Unique ID`, all_of(yield_traits)) %>% + #mutate(separator = `Unique ID`) %>% + #separate(separator, sep = c("_"), into = c("env", "IL_rep"))# + mutate(env = str_extract(string = `Unique ID`, pattern = "\\d{4}"), + gen = str_extract(string = `Unique ID`, pattern = "(?<=\\d{4}_)\\w+.*"), + gen = str_remove(gen, "\\.\\d{1,3}"), + rep = str_extract(string = `Unique ID`, pattern = "(?<=\\.)\\d{1,3}")) + +lt <- function(x){ + y <- abs(log10(x) - log10(median(x, na.rm = T))) + y +} + +yield_long <- yield %>% + pivot_longer(cols = all_of(yield_traits), + names_to = "trait", + values_to = "obs") %>% + group_by(gen, env, trait) %>% + mutate(obs = lt(obs)) + +ils <- yield %>% + distinct(gen) + +ils_vec <- ils$gen + +out <- vector(mode = "list", length = length(yield_traits)) +names <- vector(mode = "list", length = length(yield_traits)) +for(y in seq_along(yield_traits)){ + for(i in seq_along(ils_vec)) { + out[[y]][[i]] <- yield_long %>% + filter(gen %in% c(ils_vec[[i]], "M82"), trait == yield_traits[[y]], !is.na(obs)) + names[[y]][[i]] <- str_c(yield_traits[[y]], ils_vec[[i]], sep = "_") + if(all(out[[y]][[i]]$gen == "M82")) { + out[[y]][[i]] <- NA + names[[y]][[i]] <- NA + } + + } +} + +flat_out <- out %>% + purrr::list_flatten() + +flat_out_names <- names %>% + purrr::list_flatten() + +dat <- flat_out[[1]] + +fit <- lmer(obs ~ gen + (1|env) + (1|gen:env), data = dat) +results <- anova(fit) +fit.nointeraction <- lmer(obs ~ gen + (1|env), data = dat) + +results.compare <- anova(fit, fit.nointeraction) + +flat_out_filt <- discard(.x = flat_out, .p = is.na(flat_out)) +flat_out_names_filt <- discard(.x = flat_out_names, .p = is.na(flat_out_names)) + +fit <- map(.x = flat_out_filt, .f = ~lmer(obs ~ gen + (1|env) + (1|gen:env), data = .x)) +results <- map(.x = fit, .f = ~anova(.x)) +fit.nointeraction <- map(.x = flat_out_filt, .f = ~lmer(obs ~ gen + (1|env), data = .x)) +results.compare <- map2(.x = fit, .y = fit.nointeraction, .f = ~anova(.x, .y)) + +results_named <- set_names(results.compare, nm = flat_out_names_filt) + +tidy_results <- map2(.x = results.compare, .y = flat_out_names_filt, .f = ~mutate(.x, name = .y)) +tidy_res_tib <- reduce(tidy_results, bind_rows) %>% + filter(!is.na(`Pr(>Chisq)`)) + + +# Plot for supplemental --------------------------------------------------- +cb_scale <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", + "#0072B2", "#D55E00", "#CC79A7","#000000") +library(ggbeeswarm) + +il_theme <- theme(axis.text.x = element_markdown(vjust = 2, hjust = 0.5, size = 6, family = "sans", margin = margin(t = 0, r = 0, b = 0, l = 0 , unit = "mm")), + axis.text.y = element_text(size = 6, vjust = 0.5, hjust = 1, family = "sans", margin = margin(t = 0, r = 0, b = 0, l = 0 , unit = "mm")), + axis.title.x = element_text(size = 6, family = "sans", margin = margin(t = 0, r = 0, b = 0, l = 0 , unit = "mm")), + axis.title.y = element_text(size = 6, family = "sans", margin = margin(t = 0, r = 0, b = 0, l = 0 , unit = "mm")), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + strip.text = element_markdown(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")), + text = element_text(size = 6, family = "sans"), + legend.position = "bottom", + legend.text = element_markdown(size = 6), + plot.margin = unit(c(1,1,1,1), "mm"), + legend.background = element_rect(color = "white"), + legend.key = element_rect(fill = "white", color = "black", linewidth = 0.25), + legend.key.size = unit(2, "mm"), + legend.margin = margin(t = -2, r = 2, b = 0, l = 2 , unit = "mm")) + +yield_long %>% + filter(trait == "HI", gen %in% c("M82", "IL-2-5", "IL-8-3")) %>% + mutate(gen = as_factor(gen), + gen = fct_relevel(gen, c("M82", "IL-2-5", "IL-8-3"))) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot(outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) + + annotate(geom = "text", x = "IL-2-5", y = 0.5, label = "p-value = 9.42e-03") + + annotate(geom = "text", x = "IL-8-3", y = 0.5, label = "p-value = 1.24e-02") + + scale_fill_manual(aesthetics = "fill", values = cb_scale[1:2]) + + il_theme + +results_table <- tidy_res_tib %>% + separate(name, into = c("Trait", "IL"), sep = "_") %>% + select(Trait, IL, `p-value` = `Pr(>Chisq)`) %>% + pivot_wider(names_from = IL, + values_from = `p-value`) + +saveRDS(last_plot(), here("./../Manuscript_CANA1_2022/Figures/RDS_files/HI_LT.RDS")) +write.xlsx(tidy_res_tib, here("./../Manuscript_CANA1_2022/Tables/ILs_yield_levene_cmQTL.xlsx")) + +yield_long_nom <- yield %>% + pivot_longer(cols = all_of(yield_traits), + names_to = "trait", + values_to = "obs") + +yield_long %>% + filter(trait == "HI", gen %in% c("M82", "IL-8-3")) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot(outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) + + annotate(geom = "text", x = "M82", y = 0.3, label = "p.value = 0.009423311") + +yield_long_nom %>% + filter(trait == "HI", gen %in% c("M82", "IL-2-5")) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot(outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) + + annotate(geom = "text", x = "M82", y = 0.3) + +yield_long %>% + filter(trait == "BY", gen %in% c("M82", "IL-1-4")) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot() + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) + + +# Analyze with normal linear model regardless of environment---------------------------------------- + +fit <- map(.x = flat_out_filt, .f = ~lm(obs ~ gen, data = .x)) +results <- map(.x = fit, .f = ~anova(.x)) +#fit.nointeraction <- map(.x = flat_out_filt, .f = ~lmer(obs ~ gen + (1|env), data = .x)) +#results.compare <- map2(.x = fit, .y = fit.nointeraction, .f = ~anova(.x, .y)) + +#results_named <- set_names(results.compare, nm = flat_out_names_filt) + +tidy_results <- map2(.x = results, .y = flat_out_names_filt, .f = ~mutate(.x, name = .y)) +tidy_res_tib <- reduce(tidy_results, bind_rows) %>% + filter(!is.na(`Pr(>F)`)) + +results_table <- tidy_res_tib %>% + separate(name, into = c("Trait", "IL"), sep = "_") %>% + select(Trait, IL, `p-value` = `Pr(>F)`) %>% + pivot_wider(names_from = IL, + values_from = `p-value`) + +write.xlsx(results_table, here("./../Manuscript_CANA1_2022/Tables/ILs_yield_levene_cmQTL_no_env.xlsx")) + +# Test Plot --------------------------------------------------------------- + +yield_long_nom <- yield %>% + pivot_longer(cols = all_of(yield_traits), + names_to = "trait", + values_to = "obs") %>% + group_by(gen, env, trait) + +yield_long_nom %>% + filter(trait == "HI", gen %in% c("M82", "IL-2-5", "IL-8-3")) %>% + mutate(gen = as_factor(gen), + gen = fct_relevel(gen, c("M82", "IL-2-5", "IL-8-3"))) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot(outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) #+ + # annotate(geom = "text", x = "IL-2-5", y = 0.5, label = "p.value = 0.009423311") + + # annotate(geom = "text", x = "IL-8-3", y = 0.5, label = "p.value = 0.012376453") + +yield_long %>% + filter(trait == "TY", env != 2003) %>% #, gen %in% c("M82", "IL-2-5", "IL-8-3")) %>% + #mutate(gen = as_factor(gen), + # gen = fct_relevel(gen, c("M82", "IL-2-5", "IL-8-3"))) %>% + ggplot(aes(x = gen, y = obs, fill = env)) + + geom_boxplot(outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, dodge.width = 0.75) + + facet_grid(rows = vars(env)) + + theme(axis.text.x = element_text(angle = 45)) diff --git a/workflows/PS_multispeq_analysis/PS_multispeq_analysis.R b/workflows/PS_multispeq_analysis/PS_multispeq_analysis.R new file mode 100644 index 0000000000000000000000000000000000000000..e703aab4c2fedd89d7c0b5ff9260dd2349aad3f3 --- /dev/null +++ b/workflows/PS_multispeq_analysis/PS_multispeq_analysis.R @@ -0,0 +1,315 @@ +setwd(dir = dirname(rstudioapi::getActiveDocumentContext()$path)) +current <- getwd() +getwd() +library(tidyverse) +library(ggtext) +library(ggbeeswarm) +library(ggpubr) + + +# Data loading ------------------------------------------------------------ + +photo <- read_csv("spreadsheet.csv", na = "null") + +setwd("../") +sampledata <- readxl::read_xlsx("210607_WIJESI-070621-8-SlCANA1_KOs_OXs_Photosynthesis.xlsx",na = "null") +setwd(current) + +genotype_names <- read_csv("genotype_names.csv", col_types = c("cf")) + +# Data combination -------------------------------------------------------- + +params <- c("ECS_tau", "FmPrime", "FoPrime", "Fs", "FvP_over_FmP", "gH+", "kP700", + "LEF", "Light Intensity (PAR)", "NPQt", "P700_DIRK_ampl", "Phi2", + "PhiNO", "PhiNPQ", "PS1 Active Centers", "PS1 Open Centers", + "PS1 Over Reduced Centers", "PS1 Oxidized Centers", "qL", "tP700", + "v_initial_P700", "vH+", "Time of Day") + +photo_tidy <- photo %>% + filter(is.na(Bad)) %>% + mutate(aliquot = as.double(str_c(254, `LIMS ID`))) %>% + group_by(aliquot, `Leaf section color?`) %>% + mutate(first_obs = row_number()) %>% + ungroup() %>% + filter(first_obs == 1) %>% + rowwise() %>% + mutate(across(all_of(params), ~as.numeric(.x)), + #across(all_of(params), ~.x/`Light Intensity (PAR)`) + ) %>% #/`Light Intensity (PAR)` + left_join(sampledata) %>% + left_join(genotype_names) %>% + mutate(label_tidy = fct_relevel(label_tidy, c("M82", "*canal-1*", "SX", "*gh-2*", "MM", "CANAL-OX1", "CANAL-OX25", "CANAL-OX31")), + type = as_factor(`Leaf section color?`), + type = fct_relevel(type, c("green", "variegated(mixed)", "white"))) %>% + select(aliquot, plantline, label_name, label_tidy, background, type , all_of(params)) + +sampledata_tidy <- photo_tidy %>% + select(aliquot, plantline, label_name, label_tidy, background, type) %>% + mutate(plantline = as.character(plantline)) + +groups_tidy <- sampledata_tidy %>% + distinct(plantline, label_tidy, background, type) + +photo_tidy_long <- photo_tidy %>% + pivot_longer(cols = all_of(params), + names_to = "parameter", + values_to = "value") %>% + mutate(genotype_plot = str_c(label_tidy, type, sep = " "), + value = if_else(value <= 0, 0, value), + value = if_else(is.na(value), 0, value)) + +IQR <- photo_tidy_long %>% + group_by(label_name, plantline, type, parameter) %>% + summarise(Q1 = quantile(value, probs = seq(0,1, 0.25))[2], + Q3 = quantile(value, probs = seq(0,1, 0.25))[4]) %>% + ungroup() %>% + mutate(IQR = Q3-Q1) + +photo_long <- photo_tidy_long %>% + left_join(IQR) %>% + mutate(outlier = if_else(value <= Q1 - 3*IQR|value >= Q3 + 3*IQR, T, F)) %>% + filter(outlier == F) + +photo_tidy_long <- photo_long + + + + +# Statistical analysis ---------------------------------------------------- + +photo_tidy_wide <- photo_tidy_long %>% + pivot_wider(id_cols = c(plantline, label_tidy, type, aliquot), + names_from = parameter, + values_from = value) %>% + mutate(group = str_c(plantline, type, sep = "_")) + +photo_tidy_numeric <- photo_tidy_wide %>% + select(all_of(params)) + +photo_tidy_groups <- photo_tidy_long %>% + mutate(group = str_c(plantline, type, sep = "_")) + +aov_nest <- photo_tidy_groups %>% + group_by(parameter) %>% + nest() + +PAR <- photo_tidy %>% + mutate(sqrt_PAR = sqrt(`Light Intensity (PAR)`), + tod = `Time of Day`) %>% + select(plantline, label_tidy, sqrt_PAR, tod) + +aov <- aov_nest %>% + mutate(aov_PAR = map(data, .f = ~left_join(.x, PAR))) %>% + mutate(aov = map(.x = aov_PAR, .f = ~aov(.x$value ~.x$group + .x$sqrt_PAR+ .x$tod))) %>% + mutate(tidy_aov = map(aov, .f = ~broom::tidy(.x)), + sum_aov = map(aov, .f = ~summary(.x))) + +sig <- aov %>% + select(parameter, tidy_aov) %>% + unnest(cols = tidy_aov) + +aov_no_par <- aov_nest %>% + mutate(aov = map(.x = data, .f = ~aov(.x$value ~.x$group))) + +tuk <- aov %>% + select(parameter, aov) %>% + mutate(tuk = map(.x = aov, .f = ~TukeyHSD(.x, which = ".x$group")), + tuk_tidy = map(.x = tuk, .f = ~broom::tidy(.x))) + +tuk_tidy <- tuk %>% + select(parameter, tuk_tidy) %>% + unnest(cols = tuk_tidy) + +groups_tidy <- photo_tidy_groups %>% + distinct(group, plantline, label_tidy, background, type) %>% + mutate(Tissue = if_else(str_detect(type, "green"), "gl", + if_else(str_detect(type, "mixed"), "vl", "wl"))) + +tuk_tidy_sep <- tuk_tidy %>% + separate(contrast, into = c("group1", "group2"), sep = "-") %>% + left_join(groups_tidy, by = c("group1" = "group")) %>% + rename(plantline1 = plantline, label_tidy1 = label_tidy, background1 = background, type1 = type, Tissue1 = Tissue) %>% + left_join(groups_tidy, by = c("group2" = "group")) %>% + rename(plantline2 = plantline, label_tidy2 = label_tidy, background2 = background, type2 = type, Tissue2 = Tissue) %>% + filter(background1 == background2) + +# Theme ------------------------------------------------------------------- + + +com_theme <- theme(axis.text.x = element_markdown(angle = 45, hjust = 1, size = 6, family = "sans"), + axis.text.y = element_text(size = 6, family = "sans"), + axis.title.x = element_blank(), + axis.title.y = element_text(size = 6, family = "sans"), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + strip.text = element_text(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")), + text = element_text(size = 6, family = "sans"), + legend.title = element_blank(), + legend.text = element_markdown(size = 6), + plot.margin = unit(c(1,1,1,1), "mm"), + legend.margin = margin(t = 0, r = 2, b = 0, l = 2 , unit = "mm")) + + +# All plots --------------------------------------------------------------- + +y_for_p <- photo_tidy_groups %>% + group_by(parameter) %>% + summarise(y.position = 1.1*max(value)) + +sig_photo_plot <- tuk_tidy_sep %>% + #left_join(tissues, by = c("group2" = "group")) %>% + mutate(p.signif = if_else(adj.p.value <= 0.0005, "***", + if_else(adj.p.value <= 0.005, "**", + if_else(adj.p.value <= 0.05, "*", "ns")))) %>% + mutate(group1 = str_c(Tissue1, label_tidy1, sep = " "), + group2 = str_c(Tissue2, label_tidy2, sep = " "), + Tissue = Tissue1) %>% + left_join(y_for_p) + +#binwidth <- chl_plot_leaves %>% +# summarise(binwidth = (max(concentration) - min(concentration))/100) %>% +# ungroup() + +photo_tidy_plot <- photo_tidy_groups %>% + left_join(groups_tidy) %>% + mutate(group = as_factor(str_c(Tissue, label_tidy, sep = " ")), + Tissue = as_factor(Tissue), + group = fct_relevel(group, c("gl M82", "gl *canal-1*", "vl *canal-1*", "wl *canal-1*", + "gl SX", "gl *gh-2*", "vl *gh-2*", "wl *gh-2*", + "gl MM", "gl CANAL-OX1", "gl CANAL-OX25", "gl CANAL-OX31")), + type = fct_relevel(type, c("green", "variegated(mixed)", "white")), + Tissue = fct_relevel(Tissue, c("gl", "vl", "wl"))) + +plot_out <- vector("list", length = length(params)) + +for(param in seq_along(params)) { + + sig_param_temp <- sig_photo_plot %>% + filter(parameter == params[[param]]) + + plot_out[[param]] <- photo_tidy_plot %>% + filter(parameter == params[[param]]) %>% + ggplot(aes(x = group, y = value, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + #geom_beeswarm(aes(x = group, y = concentration, fill = Tissue), + # shape = 21) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_param_temp, y.position = "y.position", + step.group.by = "background1", + label = "p.signif", step.increase = 0.07, + hide.ns = T) + + #facet_grid(cols = vars(pigment_label), scales = "free") + + scale_fill_manual(values = c("darkgreen", "lightgreen", "beige")) + + ggtitle(params[[param]]) + + com_theme +} + +pdf(file = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"CANA1_OX_lines_photosynthesis_parameters.pdf"), + width = 9, + height = 4) + +for (param in seq_along(params)) { + print(plot_out[[param]]) +} + +dev.off() + + +# Write files ------------------------------------------------------------- + +library(openxlsx) + +write.xlsx(x = photo_tidy_plot, file = "Photosynq_values.xlsx") +write.xlsx(x = sig_photo_plot, file = "Photosynq_significance.xlsx") + +# Stop -------------------------------------------------------------------- + +stop() +# Plots ------------------------------------------------------------------- + +y_for_p <- photo_tidy_groups %>% + group_by(parameter) %>% + summarise(y.position = 1.1*max(value)) + +sig_photo_plot <- tuk_tidy_sep %>% + #left_join(tissues, by = c("group2" = "group")) %>% + mutate(p.signif = if_else(adj.p.value <= 0.0005, "***", + if_else(adj.p.value <= 0.005, "**", + if_else(adj.p.value <= 0.05, "*", "ns")))) %>% + mutate(group1 = str_c(Tissue1, label_tidy1, sep = " "), + group2 = str_c(Tissue2, label_tidy2, sep = " "), + Tissue = Tissue1) %>% + left_join(y_for_p)# %>% +left_join(y_for_p, by = c("var" = "pigment")) %>% + left_join(pigments, by = c("var" = "pigment")) %>% + filter(var != "ratio_chla_chlb") + +#binwidth <- chl_plot_leaves %>% +# summarise(binwidth = (max(concentration) - min(concentration))/100) %>% +# ungroup() + +photo_tidy_plot <- photo_tidy_groups %>% + left_join(groups_tidy) %>% + mutate(group = as_factor(str_c(Tissue, label_tidy, sep = " ")), + Tissue = as_factor(Tissue), + group = fct_relevel(group, c("gl M82", "gl *canal-1*", "vl *canal-1*", "wl *canal-1*", + "gl SX", "gl *gh-2*", "vl *gh-2*", "wl *gh-2*", + "gl MM", "gl CANAL-OX1", "gl CANAL-OX25", "gl CANAL-OX31")), + type = fct_relevel(type, c("green", "variegated(mixed)", "white")), + Tissue = fct_relevel(Tissue, c("gl", "vl", "wl"))) + + +sig_photo_plot_temp <- sig_photo_plot %>% + filter(parameter == "FoPrime") + +photo_tidy_plot %>% + filter(parameter == "FoPrime") %>% + ggplot(aes(x = group, y = value, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + #geom_beeswarm(aes(x = group, y = concentration, fill = Tissue), + # shape = 21) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_photo_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.07, + hide.ns = T) + + #facet_grid(cols = vars(pigment_label), scales = "free") + + scale_fill_manual(values = c("darkgreen", "lightgreen", "beige")) + + labs(x = "Tissue and genotype", y = "concentration/ µg/g FW") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_leaf_pigments.jpg"), + width = 183, + height = 100, + units = "mm", + dpi = 300) + +#saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_leaf_pigments.RDS") + +# ggplot ------------------------------------------------------------------ + +out <- vector(mode = "list", length = length(params)) + +for (i in seq_along(params)){ + + out[[i]] <- photo_tidy_long %>% + filter(parameter == params[[i]]) %>% + ggplot(aes(x = label_tidy, y = value, fill = type)) + + geom_boxplot() + + ggtitle(label = params[[i]])+ + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 8)) + + plot(out[[i]]) +} + + + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""), + ".jpg", + sep = "_"), + width = 183, + height = 100, + units = "mm", + dpi = 300) diff --git a/workflows/PS_multispeq_figures/PS_multispeq_figures.R b/workflows/PS_multispeq_figures/PS_multispeq_figures.R new file mode 100644 index 0000000000000000000000000000000000000000..8c1eb7d7a53de792ba92b810ade221f5b59a0f88 --- /dev/null +++ b/workflows/PS_multispeq_figures/PS_multispeq_figures.R @@ -0,0 +1,97 @@ +library(tidyverse) +library(ggtext) +library(ggbeeswarm) +library(ggpubr) +library(openxlsx) + + +# Data loading ------------------------------------------------------------ + +photo_tidy_plot <- read.xlsx("Photosynq_values.xlsx") +sig_photo_plot <- read.xlsx("Photosynq_significance.xlsx") + +# Theme ------------------------------------------------------------------- + + +com_theme <- theme(axis.text.x = element_markdown(angle = 45, hjust = 1, size = 6, family = "sans"), + axis.text.y = element_text(size = 6, family = "sans"), + axis.title.x = element_text(size = 6, family = "sans"), + axis.title.y = element_text(size = 6, family = "sans"), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + strip.text = element_text(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")), + text = element_text(size = 6, family = "sans"), + legend.title = element_blank(), + legend.text = element_markdown(size = 6), + plot.margin = unit(c(1,1,1,1), "mm"), + legend.margin = margin(t = 0, r = 2, b = 0, l = 2 , unit = "mm")) + + + +# Plots ------------------------------------------------------------------- + +photo_tidy_plot <- photo_tidy_plot %>% + mutate(group = fct_relevel(group, c("gl M82", "gl *canal-1*", "vl *canal-1*", "wl *canal-1*", + "gl SX", "gl *gh-2*", "vl *gh-2*", "wl *gh-2*", + "gl MM", "gl CANAL-OX1", "gl CANAL-OX25", "gl CANAL-OX31")), + type = fct_relevel(type, c("green", "variegated(mixed)", "white")), + Tissue = fct_relevel(Tissue, c("gl", "vl", "wl"))) + + +# Fs ---------------------------------------------------------------------- + + +sig_photo_plot_temp <- sig_photo_plot %>% + filter(parameter == "Fs") + +photo_tidy_plot %>% + filter(parameter == "Fs") %>% + ggplot(aes(x = group, y = value, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_photo_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.08, step.group.by = "background1", + hide.ns = T) + + scale_fill_manual(values = c("darkgreen", "lightgreen", "beige")) + + labs(x = "Tissue and genotype", y = "Fs") + + com_theme + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/Photosynq_Fs.RDS") + +# NPQt ---------------------------------------------------------------------- + +sig_photo_plot_temp <- sig_photo_plot %>% + filter(parameter == "NPQt") + +photo_tidy_plot %>% + filter(parameter == "NPQt") %>% + ggplot(aes(x = group, y = value, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_photo_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.08, step.group.by = "background1", + hide.ns = T) + + scale_fill_manual(values = c("darkgreen", "lightgreen", "beige")) + + labs(x = "Tissue and genotype", y = "NPQt") + + com_theme + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/Photosynq_NPQt.RDS") + +# Phi2 ---------------------------------------------------------------------- + +sig_photo_plot_temp <- sig_photo_plot %>% + filter(parameter == "Phi2") + +photo_tidy_plot %>% + filter(parameter == "Phi2") %>% + ggplot(aes(x = group, y = value, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_photo_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.08, step.group.by = "background1", + hide.ns = T) + + scale_fill_manual(values = c("darkgreen", "lightgreen", "beige")) + + labs(x = "Tissue and genotype", y = expression(Phi~PSII)) + + com_theme + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/Photosynq_Phi2.RDS") diff --git a/workflows/Phenotype_analysis/Phenotype_analysis.R b/workflows/Phenotype_analysis/Phenotype_analysis.R new file mode 100644 index 0000000000000000000000000000000000000000..16c94cadec2129d10cc5937d65debb9cd6983c46 --- /dev/null +++ b/workflows/Phenotype_analysis/Phenotype_analysis.R @@ -0,0 +1,567 @@ +rm(list = ls()) +library(tidyverse) +library(openxlsx) +library(lubridate) +library(broom) +library(ggtext) +library(glue) +library(ggpubr) +library(markdown) +library(cowplot) + +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +current <- getwd() +getwd() +# File loading ------------------------------------------------------------ +setwd("../") + +pt <- readxl::read_xlsx("210607_WIJESI-070621-8-SlCANA1_KOs_OXs_Photosynthesis.xlsx") + +#line_info <- readxl::read_xlsx("WIJESI-030820-13- Culturereport-051220-7624.xlsx") + +genotypes <- readxl::read_xlsx("Genotype_names.xlsx") %>% + mutate(genotype = fct_inorder(genotype)) + +pt_tidy <- pt %>% + mutate(#col_pot = as.integer(col_pot), + comment = if_else(is.na(comment), "NA", comment), + av_fruit_weight = if_else(fruit_weight != 0, fruit_weight/fruit_num, 0), + flower_date = if_else(is.na(flower_date), max(flower_date, na.rm = T) + ddays(2), flower_date), + redripe_date = if_else(is.na(redripe_date), max(redripe_date, na.rm = T) + ddays(2), redripe_date), + days_until_flower = sow_date %--% flower_date %/% ddays(1), + days_until_redripe = sow_date %--% redripe_date %/% ddays(1)) %>% + #left_join(line_info, by = c("LIMS_ID" = "aliquot")) %>% + left_join(genotypes) %>% + mutate(plantline = as_factor(plantline)) + + +pt_vars <- c(#"fruit_num_dropped", "weight_sartorius_dropped", + "fruit_num", "fruit_weight", + "av_fruit_weight", "days_until_flower", + "days_until_redripe") + +pt_long <- pt_tidy %>% + pivot_longer(cols = all_of(pt_vars), + names_to = "pt_var", + values_to = "value") %>% + group_by(plantline, alias, genotype, pt_var) %>% + mutate(rep = row_number()) %>% + filter(!is.na(value)) %>% + arrange(pt_var) %>% + ungroup() + + +# Summary statistics ------------------------------------------------------ + +pt_vars_label <- c("Total fruit number per plant", "Total fruit weight per plant/ g", + "Average fruit weight/ g", "Days until first flower", "Days until ripe fruit") + +pt_vars_plot <- cbind(pt_vars, pt_vars_label) %>% as_tibble() + +sum_stat <- pt_long %>% + #left_join(WT, by = c("irrigation", "pt_var", "rep")) %>% + group_by(plantline, genotype, alias, background, pt_var) %>% + summarise(mean = mean(value), + sd = sd(value), + n = n(), + se = sd/sqrt(n), + iqr = IQR(value), + Q1 = quantile(value)["25%"], + Q3 = quantile(value)["75%"]) %>% + ungroup() %>% + group_by( pt_var) %>% + arrange(genotype) %>% + mutate(#p_adj = p.adjust(ttest, method = "fdr"), + pt_var = as_factor(pt_var), + alias = as_factor(alias), + #irrigation = as_factor(irrigation), + genotype = as_factor(if_else(genotype == "canal1" | genotype == "gh2", glue("*{genotype}*"),glue("{genotype}"))), + genotype = fct_inorder(genotype)) %>% + ungroup() + + + +#dry weight biomass still missing +#AOV TukeyHSD? +#test manova + +pt_tidy_numeric <- pt_tidy %>% + select(all_of(pt_vars)) + +pt_aov <- map(.x = pt_tidy_numeric, .f = ~aov(.x ~ plantline , data = pt_tidy)) + +pt_aov_tidy <- pt_aov %>% + map(tidy) %>% + map2(.y = names(pt_aov), .f = ~.x %>% mutate(pt_vars = .y)) %>% + purrr::reduce(bind_rows) + +pt_aov_table <- pt_aov_tidy %>% + left_join(pt_vars_plot) %>% + filter(term != "Residuals") %>% + select(term, pt_vars_label, p.value) %>% + pivot_wider(names_from = term, values_from = p.value) + +write.xlsx(pt_aov_table, str_c(str_replace_all(Sys.Date(),"^.{2}|-",""), + "anova_table.xlsx", + sep = "_")) + +pt_tuk <- map(.x = pt_aov, .f = ~TukeyHSD(.x)) %>% + map(.f = tidy) %>% + map2(.y = names(pt_aov), .f = ~.x %>% mutate(var = .y)) %>% + purrr::reduce(bind_rows) + +# Pairwise significant changes with t-tests ------------------------------- + +setwd(current) + +pt_tidy_numeric <- pt_tidy %>% + select(all_of(pt_vars)) + +pt_ttest <- map(.x = pt_tidy_numeric, + .f = ~pairwise.t.test(.x, g = pt_tidy$plantline , + data = pt_tidy, p.adjust.method = "none", pool.sd = F)) + +pt_ttest_tidy <- pt_ttest %>% + map(tidy) %>% + map2(.y = names(pt_ttest), .f = ~.x %>% mutate(pt_vars = .y)) %>% + purrr::reduce(bind_rows) + +sig_pt <- pt_ttest_tidy %>% + filter(group1 == "992725" | group2 == "992725"| + group1 == "1012066" | group2 == "1012066"| + group1 == "1018733" | group2 == "1018733") %>% + ungroup() %>% + left_join(sum_stat, by = c("pt_vars" = "pt_var", "group1" = "plantline")) %>% + select(pt_vars, group1, group2, p.value, mean1 = mean, se1 = se, genotype1 = genotype, background1 = background) %>% + left_join(sum_stat, by = c("pt_vars" = "pt_var", "group2" = "plantline")) %>% + select(pt_vars,p.value, mean1, mean2 = mean, se1, se2 = se, genotype1, genotype2 = genotype, background1, background2 = background) %>% + filter(background1 == background2) %>% + group_by(pt_vars) %>% + mutate(tot_val1 = mean1 + se1, + tot_val2 = mean2 + se2, + y.position = 1.1*(max(tot_val1, tot_val2)), + padj = p.adjust(p.value, method = "fdr"), + p.signif = if_else(p.value <= 0.05, "*", "ns")) %>% + rename(pt_var = pt_vars, + group1 = genotype1, + group2 = genotype2) %>% + mutate(pt_var = as_factor(pt_var), + group1 = as_factor(group1), + group2 = as_factor(group2)) %>% + ungroup() + + + +pt_vars + +plot_out <- vector("list", length = length(pt_vars)) + +for(pt in seq_along(pt_vars)) { + sig_pt_temp <- sig_pt %>% + filter(pt_var == pt_vars[[pt]]) + + plot_out[[pt]] <- sum_stat %>% + filter(pt_var == pt_vars[[pt]]) %>% + ggplot(aes(genotype, mean)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(width = 0.9), width = 0.75) + + #facet_grid(cols = vars(irrigation), scales = "fixed") + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.1, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "right") + + ggtitle(pt_vars_label[[pt]]) + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") +} + +pdf(file = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"CANA1_OX_lines_phenotypes.pdf"), + width = 9, + height = 4) + +for (pt in seq_along(pt_vars)) { + print(plot_out[[pt]]) +} + +dev.off() + + +sum_stat %>% + ggplot(aes(genotype, mean, color = genotype)) + + geom_point() + + geom_line(aes(group = plantline)) + + geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd)) + + #geom_text(aes(label = is_sig), nudge_y = 10) + + facet_wrap(facets = vars(pt_var),scales = "free") + + + +# All traits in one plot -------------------------------------------------- + +max_val <- pt_long %>% + group_by(background, pt_var) %>% + summarise(max_val = 1.01*max(value)) + +sig_pt_plot <- sig_pt %>% + left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + left_join(max_val, by = c("pt_var", "background1" = "background")) %>% + mutate(y.position = max_val) + +pt_long %>% + arrange(genotype) %>% + mutate(genotype = as_factor(if_else(genotype == "canal1" | genotype == "gh2", glue("*{genotype}*"),glue("{genotype}"))), + genotype = fct_inorder(genotype)) %>% + left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + ggplot(aes(genotype, value, color = genotype)) + + geom_boxplot() + + stat_pvalue_manual(sig_pt_plot, label = "p.signif", y.position = "y.position", + step.increase = 0.1, tip.length = 0.01, + hide.ns = T, + step.group.by = "pt_var") + + facet_wrap(nrow = 1, ncol = 5, facets = vars(pt_vars_label),scales = "free")+ + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 8), + legend.text = element_markdown(), + legend.position = "bottom") + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""), + "CANA1_OX_lines_phenotypic.jpg", + sep = "_"), + width = 183, + height = 100, + units = "mm", + dpi = 300) + + +# Write to file ----------------------------------------------------------- + +genotypes_plot <- read_csv("genotype_names.csv") + +pt_long_tidy <- pt_long %>% + left_join(genotypes_plot) + +genotypes_old <- pt_long_tidy %>% + distinct(genotype, label_tidy) + +sig_pt_tidy <- sig_pt_plot %>% + mutate(group1 = str_remove_all(group1, "\\*"), + group2 = str_remove_all(group2, "\\*")) %>% + left_join(genotypes_old, by = c("group1" = "genotype")) %>% + rename(group1_old = group1, + group1 = label_tidy) %>% + left_join(genotypes_old, by = c("group2" = "genotype")) %>% + rename(group2_old = group2, + group2 = label_tidy) + +write.xlsx(pt_long_tidy, "phenotype_values.xlsx") +write.xlsx(sig_pt_tidy, "phenotype_significance.xlsx") + +# Pairwise significant changes MAD-scaled ------------------------------- + +setwd(current) + +pt_tidy_mad <- pt_long %>% + filter(background != "Sioux") %>% + group_by(genotype, pt_var) %>% + mutate(mad = abs(value-median(value))/median(value)) %>% + ungroup() + +pt_tidy_mad_wide <- pt_tidy_mad %>% + pivot_wider(id_cols = c(aliquot, plantline, genotype, alias, genotype, background), + names_from = pt_var, + values_from = mad) + +pt_tidy_numeric <- pt_tidy_mad_wide %>% + select(all_of(pt_vars)) + +pt_ttest <- map(.x = pt_tidy_numeric, + .f = ~pairwise.t.test(.x, g = pt_tidy_mad_wide$plantline , + data = pt_tidy_mad_wide, p.adjust.method = "none", pool.sd = F)) + +pt_ttest_tidy <- pt_ttest %>% + map(tidy) %>% + map2(.y = names(pt_ttest), .f = ~.x %>% mutate(pt_vars = .y)) %>% + purrr::reduce(bind_rows) + +sig_pt <- pt_ttest_tidy %>% + filter(group1 == "992725" | group2 == "992725"| + group1 == "1012066" | group2 == "1012066"| + group1 == "1018733" | group2 == "1018733") %>% + ungroup() %>% + left_join(sum_stat, by = c("pt_vars" = "pt_var", "group1" = "plantline")) %>% + select(pt_vars, group1, group2, p.value, mean1 = mean, se1 = se, genotype1 = genotype, background1 = background) %>% + left_join(sum_stat, by = c("pt_vars" = "pt_var", "group2" = "plantline")) %>% + select(pt_vars,p.value, mean1, mean2 = mean, se1, se2 = se, genotype1, genotype2 = genotype, background1, background2 = background) %>% + filter(background1 == background2) %>% + group_by(pt_vars) %>% + mutate(tot_val1 = mean1 + se1, + tot_val2 = mean2 + se2, + y.position = 1.1*(max(tot_val1, tot_val2)), + padj = p.adjust(p.value, method = "fdr"), + p.signif = if_else(p.value <= 0.05, "*", "ns")) %>% + rename(pt_var = pt_vars, + group1 = genotype1, + group2 = genotype2) %>% + mutate(pt_var = as_factor(pt_var), + group1 = as_factor(group1), + group2 = as_factor(group2)) %>% + ungroup() + + + +pt_vars + +plot_out <- vector("list", length = length(pt_vars)) + +for(pt in seq_along(pt_vars)) { + sig_pt_temp <- sig_pt %>% + filter(pt_var == pt_vars[[pt]]) + + plot_out[[pt]] <- sum_stat %>% + filter(pt_var == pt_vars[[pt]]) %>% + ggplot(aes(genotype, mean)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(width = 0.9), width = 0.75) + + #facet_grid(cols = vars(irrigation), scales = "fixed") + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.1, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "right") + + ggtitle(pt_vars_label[[pt]]) + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") +} + +pdf(file = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"CANA1_OX_lines_phenotypes.pdf"), + width = 9, + height = 4) + +for (pt in seq_along(pt_vars)) { + print(plot_out[[pt]]) +} + +dev.off() + + +sum_stat %>% + ggplot(aes(genotype, mean, color = genotype)) + + geom_point() + + geom_line(aes(group = plantline)) + + geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd)) + + #geom_text(aes(label = is_sig), nudge_y = 10) + + facet_wrap(facets = vars(pt_var),scales = "free") + + + +# All traits in one plot -------------------------------------------------- + +max_val <- pt_tidy_mad %>% + group_by(background, pt_var) %>% + summarise(max_val = 1.01*max(mad)) + +sig_pt_plot <- sig_pt %>% + left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + left_join(max_val, by = c("pt_var", "background1" = "background")) %>% + mutate(y.position = max_val) + +pt_tidy_mad %>% + arrange(genotype) %>% + mutate(genotype = as_factor(if_else(genotype == "canal1" | genotype == "gh2", glue("*{genotype}*"),glue("{genotype}"))), + genotype = fct_inorder(genotype)) %>% + left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + ggplot(aes(genotype, mad, color = genotype)) + + geom_boxplot() + + stat_pvalue_manual(sig_pt_plot, label = "p.signif", y.position = "y.position", + step.increase = 0.1, tip.length = 0.01, + hide.ns = T, + step.group.by = "pt_var") + + facet_wrap(nrow = 1, ncol = 5, facets = vars(pt_vars_label),scales = "free")+ + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 8), + legend.text = element_markdown(), + legend.position = "bottom") + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""), + "CANA1_OX_lines_phenotypic.jpg", + sep = "_"), + width = 183, + height = 100, + units = "mm", + dpi = 300) + +stop("end of pipeline") + +# Figure 40 --------------------------------------------------------------- + +sig_pt_temp <- sig_pt %>% + filter(pt_var == "total_fruit_weight") + +p1<- sum_stat %>% + filter(pt_var == "total_fruit_weight") %>% + ggplot(aes(genotype, mean)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(width = 0.9), width = 0.25) + + facet_grid(cols = vars(irrigation), scales = "fixed") + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.07, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "", + plot.margin = unit(c(0,0,0,0), "cm")) + + xlab("") + + ylim(0,1150) + + ggtitle("Total fruit weight per plant/ g") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") + +p1 + +sig_pt_temp <- sig_pt %>% + filter(pt_var == "av_fruit_weight") + +p2<- sum_stat %>% + filter(pt_var == "av_fruit_weight") %>% + ggplot(aes(genotype, mean)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(width = 0.9), width = 0.25) + + facet_grid(cols = vars(irrigation), scales = "fixed") + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.07, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "", + plot.margin = unit(c(0,0,0,0), "cm")) + + xlab("") + + ylim(c(0,65))+ + ggtitle("Average fruit weight/ g") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") + +p2 + +leg40 <- get_legend(sum_stat %>% + filter(pt_var == "av_fruit_weight") %>% + ggplot(aes(genotype, mean)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean - se, ymax = mean + se), position = position_dodge(width = 0.9), width = 0.25) + + facet_grid(cols = vars(irrigation), scales = "fixed") + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.07, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "right") + + ggtitle("Average fruit weight/ g") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill")) +leg40 + +main_plot <- plot_grid(p1,p2, ncol = 1, labels = "AUTO") +main_plot + +comp_plot <- plot_grid(main_plot, leg40, ncol = 2, rel_widths = c(3,1)) +comp_plot + +ggsave("figure_40.wmf",width = 15.8, height = 12, units = "cm", dpi = 300) + +# Figure 41 --------------------------------------------------------------- + + +sig_pt_temp <- sig_pt_cv_groups %>% + filter(pt_var == "av_fruit_weight") + +p1 <- cv_jack_sum_stat %>% + filter(pt_var == "av_fruit_weight") %>% + ggplot(aes(genotype, mean_cv)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean_cv - se, ymax = mean_cv + se), position = position_dodge(width = 0.9), width = 0.25) + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.1, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "", + plot.title = element_text(size = 14), + plot.margin = unit(c(0,0,1,0), "mm")) + + ggtitle("Average fruit weight/ g") + + xlab("") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") + + ylab("mean CV") +p1 + +sig_pt_temp <- sig_pt_cv_groups %>% + filter(pt_var == "harvest_index") + +p2 <- cv_jack_sum_stat %>% + filter(pt_var == "harvest_index") %>% + ggplot(aes(genotype, mean_cv)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean_cv - se, ymax = mean_cv + se), position = position_dodge(width = 0.9), width = 0.25) + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.1, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "", + plot.title = element_text(size = 14), + plot.margin = unit(c(0,0,1,0), "mm")) + + ggtitle("Harvest index") + + xlab("") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") + + ylab("mean CV") +p2 + +leg41 <- get_legend(cv_jack_sum_stat %>% + filter(pt_var == "harvest_index") %>% + ggplot(aes(genotype, mean_cv)) + + geom_col(position = "dodge", color = "black", aes(fill = genotype)) + + geom_errorbar(aes(ymin = mean_cv - se, ymax = mean_cv + se), position = position_dodge(width = 0.9), width = 0.25) + + stat_pvalue_manual(sig_pt_temp, label = "p.signif", y.position = "y.position", + step.increase = 0.1, + hide.ns = T) + + theme(axis.text.x = element_markdown(angle = 45, hjust = 1), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + text = element_text(size = 14), + legend.text = element_markdown(), + legend.position = "bottom") + + ggtitle("Harvest index") + + scale_fill_grey(start = 1, end = 0, aesthetics = "fill") + + ylab("mean CV")) + + + +main_plot <- plot_grid(p1,p2, ncol = 2, labels = "AUTO") +main_plot + +comp_plot <- plot_grid(main_plot, leg41, ncol = 1, rel_heights = c(10,1)) + theme(plot.margin = unit(c(0,0,5,0), "mm")) +comp_plot + +ggsave("figure_41.wmf",width = 15.8, height = 8, units = "cm", dpi = 300) + diff --git a/workflows/Phenotype_figures/Phenotype_figures.R b/workflows/Phenotype_figures/Phenotype_figures.R new file mode 100644 index 0000000000000000000000000000000000000000..49d00f95a8771074ef8ed4039c62dbea81ed02f2 --- /dev/null +++ b/workflows/Phenotype_figures/Phenotype_figures.R @@ -0,0 +1,99 @@ +library(tidyverse) +library(ggtext) +library(ggbeeswarm) +library(ggpubr) +library(openxlsx) + + +# Data loading ------------------------------------------------------------ + +pheno_tidy_plot <- read.xlsx("phenotype_values.xlsx") +sig_pheno_plot <- read.xlsx("phenotype_significance.xlsx") %>% + mutate(p.signif = if_else(p.value <= 0.0005, "***", + if_else(p.value <= 0.005, "**", + if_else(p.value <= 0.05, "*", "ns")))) + +# Theme ------------------------------------------------------------------- + + +com_theme <- theme(axis.text.x = element_markdown(angle = 45, hjust = 1, size = 6, family = "sans"), + axis.text.y = element_text(size = 6, family = "sans"), + axis.title.x = element_text(size = 6, family = "sans"), + axis.title.y = element_text(size = 6, family = "sans"), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + strip.text = element_text(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")), + text = element_text(size = 6, family = "sans"), + legend.title = element_blank(), + legend.text = element_markdown(size = 6), + plot.margin = unit(c(1,1,1,1), "mm"), + legend.margin = margin(t = 0, r = 2, b = 0, l = 2 , unit = "mm")) + + + +# Plots ------------------------------------------------------------------- + +pheno_tidy_plot <- pheno_tidy_plot %>% + #rename(group = label_tidy) %>% + mutate(group = as_factor(group), + group = fct_relevel(group, c("M82", "*canal-1*", + "SX", "*gh-2*", + "MM", "CANAL-OX1", "CANAL-OX25", "CANAL-OX31"))) + + +# fruit_weight ---------------------------------------------------------------------- + +max_val <- pheno_tidy_plot %>% + group_by(background, pt_var) %>% + summarise(max_val = 1.1*max(value)) %>% + ungroup() + +sig_pheno_plot_temp <- sig_pheno_plot %>% + filter(pt_var == "fruit_weight") %>% + mutate(genotype = group1) %>% + select(-max_val) %>% + #left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + left_join(max_val, by = c("pt_var", "background1" = "background")) %>% + mutate(y.position = max_val) + +pheno_tidy_plot %>% + mutate(genotype = group) %>% + filter(pt_var == "fruit_weight") %>% + ggplot(aes(x = group, y = value, fill = genotype)) + + geom_boxplot(size = 0.2, outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 2) + + stat_pvalue_manual(sig_pheno_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.08, step.group.by = "background1", + hide.ns = T) + + scale_fill_manual(values = c("darkgreen", "beige", "darkgreen", "beige","darkgreen", "darkgreen", "darkgreen", "darkgreen")) + + labs(x = "Tissue and genotype", y = "Total fruit weight (g)") + + com_theme + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/OX_phenotype_fruit_weight.RDS") + + +# Everything BUT fruit weight for supplement ------------------------------ + +sig_pheno_plot_temp <- sig_pheno_plot %>% + filter(pt_var != "fruit_weight") %>% + mutate(genotype = group1) %>% + select(-max_val) %>% + #left_join(pt_vars_plot, by = c("pt_var" = "pt_vars")) %>% + left_join(max_val, by = c("pt_var", "background1" = "background")) %>% + mutate(y.position = max_val) + +pheno_tidy_plot %>% + mutate(genotype = group) %>% + filter(pt_var != "fruit_weight") %>% + ggplot(aes(x = group, y = value, fill = genotype)) + + geom_boxplot(size = 0.2, outlier.shape = NA) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 2) + + stat_pvalue_manual(sig_pheno_plot_temp, y.position = "y.position", + label = "p.signif", step.increase = 0.08, step.group.by = "background1", + hide.ns = T) + + scale_fill_manual(values = c("darkgreen", "beige", "darkgreen", "beige","darkgreen", "darkgreen", "darkgreen", "darkgreen")) + + labs(x = "Tissue and genotype", y = "Total fruit weight (g)") + + facet_wrap(facets = vars(pt_var)) + + com_theme + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/OX_phenotype_rest.RDS") diff --git a/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R b/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R new file mode 100644 index 0000000000000000000000000000000000000000..05be75e479ac717e180e647a28e1ecf34359fdd4 --- /dev/null +++ b/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R @@ -0,0 +1,734 @@ +rm(list = ls()) +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +getwd() +library(tidyverse) +library(car) +library(broom) +library(ggtext) +library(ggpubr) +library(openxlsx) +library(ggbeeswarm) + +sampledata <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx", + sheet = 2) +extraction <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx", + sheet = 3) +treatment <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx", + sheet = 4) + +av_yield <- treatment %>% + filter(Name != "no_LIMS") %>% + mutate(fruit_yield = as.numeric(fruit_yield)) %>% + group_by(genotype,trtmnt) %>% + summarise(av_yield = mean(fruit_yield, na.rm = T), + sd_yield = sd(fruit_yield, na.rm = T), + obs = n()) %>% + mutate(cv_yield = sd_yield/av_yield) + +iCV <- av_yield %>% + pivot_wider(id_cols = c(genotype), + names_from = trtmnt, + values_from = cv_yield) %>% + mutate(iCV = drought/control) + +treatment <- treatment %>% + filter(Name != "no_LIMS") %>% + mutate(genotype = as_factor(genotype), + trtmnt = as_factor(trtmnt)) + +yield_control <- treatment %>% + filter(trtmnt == "control") + +yield_drought <- treatment %>% + filter(trtmnt == "drought") + +plot_label <- tribble(~genotype, ~group, + "M82", "M82", + "CANA1", "*canal-1*") + +y_for_p <- treatment %>% + group_by(trtmnt) %>% + summarise(y.pos = 1.1*max(fruit_yield)) + +p_ctrl <- tibble(p_value = kruskal.test(fruit_yield ~ genotype , data = yield_control)$p.value) %>% + mutate(trtmnt = "control") + +p_drought <- tibble(p_value = kruskal.test(fruit_yield ~ genotype , data = yield_drought)$p.value) %>% + mutate(trtmnt = "drought") + +yield_p <- bind_rows(p_ctrl, p_drought) + +sig_yield_plot <- y_for_p %>% + left_join(yield_p) %>% + mutate(group1 = "M82", + group2 = "*canal-1*", + group = group1, + p.signif = if_else(p_value <= 0.0005, "***", + if_else(p_value <= 0.005, "**", + if_else(p_value <= 0.05, "*", "ns")))) %>% + mutate(y = "fruit_yield") + + +# Theme ------------------------------------------------------------------- + + +com_theme <- theme(axis.text.x = element_markdown(angle = 45, hjust = 1, size = 6, family = "sans"), + axis.text.y = element_text(size = 6, family = "sans"), + axis.title.x = element_blank(), + axis.title.y = element_text(size = 6, family = "sans"), + panel.background = element_rect(fill = "white"), + panel.border = element_rect(color = "black",fill = NA), + strip.text = element_text(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")), + text = element_text(size = 6, family = "sans"), + legend.title = element_blank(), + legend.text = element_markdown(size = 6), + plot.margin = unit(c(1,1,1,1), "mm"), + legend.margin = margin(t = 0, r = 2, b = 0, l = 2 , unit = "mm")) + + +# Yield plot -------------------------------------------------------------- + + +treatment <- treatment %>% + left_join(plot_label) + +binwidth <- treatment %>% + summarise(binwidth = (max(fruit_yield) - min(fruit_yield))/50) + +treatment %>% + mutate(group = as_factor(group), + trtmnt = as_factor(trtmnt)) %>% + ggplot(aes(x = group, y = fruit_yield, fill = group)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + #geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) + + stat_pvalue_manual(data = sig_yield_plot, y.position = "y.pos", label = "p.signif", xmin = "group1", xmax = "group2") + + facet_grid(cols = vars(trtmnt), scales = "free") + + scale_fill_manual(values = c("darkgreen", "beige")) + + labs(x = "Genotype", y = "Fruit yield/ g/plant") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield.jpg"), + width = 89, + height = 60, + units = "mm", + dpi = 300) + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield.RDS") + +sum_stat <- treatment %>% + group_by(trtmnt, group) %>% + summarise(mean = mean(fruit_yield), + sd = sd(fruit_yield), + cv = sd/mean, + n = n()) %>% + ungroup() %>% + mutate(group = str_remove_all(group, "\\*"), + mean = round(mean, digits = 4), + sd = round(sd, digits = 4), + cv = round(cv, digits = 4)) %>% + select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, CV = cv, N = n) %>% + mutate(Genotype = as_factor(Genotype), + Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>% + arrange(Treatment, Genotype) + +write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_sum_stat.xlsx") + +# Test MAD ------------------------------------------------------ + +mad <- treatment %>% + group_by(trtmnt, genotype) %>% + mutate(mad = abs(fruit_yield - median(fruit_yield))/median(fruit_yield)) %>% + ungroup() + +mad_nest <- mad %>% + group_by(trtmnt) %>% + nest() + +mad_p <- mad_nest %>% + mutate(krusk = map(data, .f = ~kruskal.test(.x$mad ~ .x$genotype)$p.value)) %>% + select(trtmnt, krusk) + +y_for_p <- mad %>% + group_by(trtmnt) %>% + summarise(y.pos = 1.1*max(mad)) + +sig_yield_plot <- mad_p %>% + left_join(y_for_p) %>% + mutate(group1 = "M82", + group2 = "*canal-1*", + group = group1, + p.signif = if_else(krusk <= 0.0005, "***", + if_else(krusk <= 0.005, "**", + if_else(krusk <= 0.05, "*", "ns")))) %>% + mutate(y = "mad") + +binwidth <- mad %>% + summarise(binwidth = (max(mad) - min(mad))/50) + +mad %>% + mutate(group = as_factor(group), + trtmnt = as_factor(trtmnt)) %>% + ggplot(aes(x = group, y = mad, fill = group)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + #geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) + + stat_pvalue_manual(data = sig_yield_plot, hide.ns = T, y.position = "y.pos", label = "p.signif") + + facet_grid(cols = vars(trtmnt), scales = "free") + + scale_fill_manual(values = c("darkgreen", "beige")) + + labs(x = "Genotype", y = "Scaled MAD Fruit yield/ g/plant") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield_MAD.jpg"), + width = 89, + height = 60, + units = "mm", + dpi = 300) + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield_mad_scaled.RDS") + +sum_stat <- mad %>% + group_by(trtmnt, group) %>% + summarise(mean = mean(mad), + sd = sd(mad), + cv = sd/mean, + n = n()) %>% + ungroup() %>% + mutate(group = str_remove_all(group, "\\*"), + mean = round(mean, digits = 4), + sd = round(sd, digits = 4), + cv = round(cv, digits = 4)) %>% + select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, N = n) %>% + mutate(Genotype = as_factor(Genotype), + Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>% + arrange(Treatment, Genotype) + +write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_MAD_sum_stat.xlsx") + + +# Test Levene ------------------------------------------------------ +skip = T +if(skip == T){ + print("skipped") +} else{ +lev <- treatment %>% + group_by(trtmnt, genotype) %>% + mutate(lev = abs(log10(fruit_yield) - log10(median(fruit_yield)))) %>% + ungroup() + +lev_nest <- lev %>% + group_by(trtmnt) %>% + nest() + +lev_p <- lev_nest %>% + mutate(krusk = map(data, .f = ~kruskal.test(.x$lev ~ .x$genotype)$p.value)) %>% + select(trtmnt, krusk) + +y_for_p <- lev %>% + group_by(trtmnt) %>% + summarise(y.pos = 1.1*max(lev)) + +sig_yield_plot <- lev_p %>% + left_join(y_for_p) %>% + mutate(group1 = "M82", + group2 = "*canal-1*", + group = group1, + p.signif = if_else(krusk <= 0.0005, "***", + if_else(krusk <= 0.005, "**", + if_else(krusk <= 0.05, "*", "ns")))) %>% + mutate(y = "lev") + +binwidth <- lev %>% + summarise(binwidth = (max(lev) - min(lev))/50) + +lev %>% + mutate(group = as_factor(group), + trtmnt = as_factor(trtmnt)) %>% + ggplot(aes(x = group, y = lev, fill = group)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) + + stat_pvalue_manual(data = sig_yield_plot, hide.ns = T, y.position = "y.pos", label = "p.signif") + + facet_grid(cols = vars(trtmnt), scales = "free") + + scale_fill_manual(values = c("darkgreen", "beige")) + + labs(x = "Genotype", y = "Scaled lev Fruit yield/ g/plant") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield_lev.jpg"), + width = 89, + height = 60, + units = "mm", + dpi = 300) + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield_lev_scaled.RDS") + +sum_stat <- lev %>% + group_by(trtmnt, group) %>% + summarise(mean = mean(lev), + sd = sd(lev), + cv = sd/mean, + n = n()) %>% + ungroup() %>% + mutate(group = str_remove_all(group, "\\*"), + mean = round(mean, digits = 4), + sd = round(sd, digits = 4), + cv = round(cv, digits = 4)) %>% + select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, N = n) %>% + mutate(Genotype = as_factor(Genotype), + Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>% + arrange(Treatment, Genotype) + +write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_lev_sum_stat.xlsx") +} + +# Chlorophyll ------------------------------------------------------------- + +chl1 <- readxl::read_excel("210128_Chl_CANA1_1_to_10.xlsx", + sheet = 2) + +chl1_reformat <- chl1 %>% + pivot_longer(cols = matches("^\\d{1,2}$"), + names_to = "column", + values_to = "value") %>% + pivot_wider(id_cols = c(row,column), + names_from = identity, + values_from = value) + +chl2 <- readxl::read_excel("210130_Chl_CANA1_1_to_10.xlsx", + sheet = 2) + +chl2_reformat <- chl2 %>% + pivot_longer(cols = matches("^\\d{1,2}$"), + names_to = "column", + values_to = "value") %>% + pivot_wider(id_cols = c(row,column), + names_from = identity, + values_from = value) + +chl3 <- readxl::read_excel("210131_Chl_CANA1_1_to_10.xlsx", + sheet = 2) + +chl3_reformat <- chl3 %>% + pivot_longer(cols = matches("^\\d{1,2}$"), + names_to = "column", + values_to = "value") %>% + pivot_wider(id_cols = c(row,column), + names_from = identity, + values_from = value) + +chl_all <- chl1_reformat %>% + bind_rows(chl2_reformat,chl3_reformat) %>% + filter(!is.na(extraction_num)) + +blank <- chl_all %>% + filter(extraction_num == 0) %>% + summarise(blank_652 = mean(`652`), + blank_665 = mean(`665`), + blank_470 = mean(`470`)) + +sampledata_tidy <- sampledata %>% + left_join(extraction) %>% + left_join(treatment, by = "Aliquot ID") %>% + mutate(Genotype = as_factor(Genotype)) + +write_csv(sampledata_tidy, str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"sampledata_tidy.csv")) + +levels(sampledata_tidy$Genotype) +sampledata_tidy$Genotype <- ordered(sampledata_tidy$Genotype, + levels = c("M82", "CANA1", "CRISPR_8619", "CRISPR_7472")) + +chl_calc <- chl_all %>% + left_join(sampledata_tidy) %>% + filter(trtmnt == "control") %>% + mutate(path_652 = (`652`- blank$blank_652)/0.51, + path_665 = (`665`- blank$blank_665)/0.51, + path_470 = (`470`- blank$blank_470)/0.51, + chl_a = ((16.72 * path_665 - 9.16 * path_652)), + chl_b = ((34.09 * path_652 - 15.28 * path_665)), + car_tot = (((1000 * path_470 - 1.63 * chl_a - 104.96 * chl_b) / 221)), + chla_ug_g = if_else(chl_a < 0 , 0, (chl_a*2.5/fw) *1000), + chlb_ug_g = if_else(chl_b < 0 , 0, (chl_b*2.5/fw) *1000), + car_tot_ug_g = if_else(car_tot < 0, 0, (car_tot*2.5/fw) *1000), + ratio_chla_chlb = chla_ug_g/chlb_ug_g, + #ratio_chla_chlb = if_else(is.finite(ratio_chla_chlb), ratio_chla_chlb, 1), + Tissue = as_factor(Tissue), + tissue_group = as_factor(str_extract(Tissue, "(?<!\\w)\\w+$")), + tissue_short = abbreviate(Tissue, minlength = 2), + group_anova = as_factor(str_c(tissue_short,group, sep =" "))) %>% + filter(is.finite(ratio_chla_chlb)) + +levels(chl_calc$group_anova) + +chl_calc$group_anova <- fct_relevel(chl_calc$group_anova, + c("gl M82", + "gl *canal-1*", + "wl *canal-1*", + "st M82", + "st *canal-1*")) + +chl_calc %>% + filter(tissue_group == "stems") %>% + ggplot(aes(x = Tissue, y = chla_ug_g, fill = genotype)) + + geom_dotplot(binaxis = "y", stackdir = "center", position = "dodge") + +chl_plot <- chl_calc %>% + pivot_longer(cols = c(contains("ug_g"), ratio_chla_chlb), + names_to = "pigment", + values_to = "concentration") %>% + mutate(pigment = as_factor(pigment)) + +sum_stats <- chl_plot %>% + group_by(pigment, Tissue, genotype) %>% + summarise(norm_dist = shapiro.test(concentration)$p.value, + mean = mean(concentration), + sd = sd(concentration), + n = n()) %>% + mutate(se = sd/sqrt(n)) + +data_leaves <- chl_calc %>% + filter(tissue_group == "leaves") %>% + select(group_anova, chla_ug_g, chlb_ug_g,car_tot_ug_g,ratio_chla_chlb) + +numeric_data_leaves <- data_leaves %>% + select(where(is.numeric)) + +map_dfc(.x = numeric_data_leaves, .f = ~leveneTest(.x ~ data_leaves$group_anova)) +map_dfc(.x = numeric_data_leaves, .f = ~kruskal.test(.x ~ data_leaves$group_anova)$p.value) +leaves_wilcox <- map(.x = numeric_data_leaves, .f = ~pairwise.wilcox.test(.x, data_leaves$group_anova)) + +sig_leaves <- leaves_wilcox %>% + map(.f = tidy) %>% + map2(.y = names(leaves_wilcox), .f = ~.x %>% mutate(var = .y)) %>% + purrr::reduce(bind_rows) + +data_stems <- chl_calc %>% + filter(tissue_group == "stems") %>% + select(group_anova, chla_ug_g, chlb_ug_g,car_tot_ug_g, ratio_chla_chlb) + +numeric_data_stems <- data_stems %>% + select(where(is.numeric)) + +map_dfc(.x = numeric_data_stems, .f = ~leveneTest(.x ~ data_stems$group_anova)) +map_dfc(.x = numeric_data_stems, .f = ~kruskal.test(.x ~ data_stems$group_anova)$p.value) +stems_t.test <- map(.x = numeric_data_stems, .f = ~pairwise.t.test(.x, data_stems$group_anova)) + +sig_stems <- stems_t.test %>% + map(.f = tidy) %>% + map2(.y = names(stems_t.test), .f = ~.x %>% mutate(var = .y)) %>% + purrr::reduce(bind_rows) + +#single tests +#leveneTest(chla_ug_g ~ group_anova, data = data_chl_a_leaves) +#kruskal.test(chla_ug_g ~ group_anova, data = data_chl_a_leaves)$p.value +#pairwise.wilcox.test(data_chl_a_leaves$chla_ug_g, + # data_chl_a_leaves$group_anova) + +pigments <- tibble( + chla_ug_g = "chlorophyll a", + chlb_ug_g = "chlorophyll b", + car_tot_ug_g = "total carotenoids", + ratio_chla_chlb = "chlorophyll a/b ratio") %>% + pivot_longer(cols = everything(), + names_to = "pigment", + values_to = "pigment_label") + + +# Leaves pigment plot ----------------------------------------------------- + +chl_plot_leaves <- chl_plot %>% + filter(pigment != "ratio_chla_chlb", + tissue_group == "leaves") %>% + mutate(group = group_anova) %>% + left_join(pigments) + +tissues <- chl_plot_leaves %>% + distinct(group, Tissue) + +y_for_p <- chl_plot_leaves %>% + group_by(pigment) %>% + summarise(y.position = 1.1*max(concentration)) + +sig_leaves_plot <- sig_leaves %>% + left_join(tissues, by = c("group2" = "group")) %>% + mutate(p.signif = if_else(p.value <= 0.0005, "***", + if_else(p.value <= 0.005, "**", + if_else(p.value <= 0.05, "*", "ns")))) %>% + left_join(y_for_p, by = c("var" = "pigment")) %>% + left_join(pigments, by = c("var" = "pigment")) %>% + filter(var != "ratio_chla_chlb") + +binwidth <- chl_plot_leaves %>% + summarise(binwidth = (max(concentration) - min(concentration))/100) %>% + ungroup() + +chl_plot_leaves %>% + ggplot(aes(x = group, y = concentration, fill = Tissue)) + + geom_boxplot(size = 0.2, outlier.size = 0.2) + + #geom_beeswarm(aes(x = group, y = concentration, fill = Tissue), + # shape = 21) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_leaves_plot, y.position = "y.position", + label = "p.signif", step.increase = 0.07, + hide.ns = T) + + facet_grid(cols = vars(pigment_label), scales = "free") + + scale_fill_manual(values = c("darkgreen", "beige")) + + labs(x = "Tissue and genotype", y = "concentration/ µg/g FW") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_leaf_pigments.jpg"), + width = 183, + height = 100, + units = "mm", + dpi = 300) + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_leaf_pigments.RDS") + +sum_stat <- chl_plot_leaves %>% + group_by(group, pigment_label) %>% + summarise(mean = mean(concentration), + sd = sd(concentration), + cv = sd/mean, + n = n()) %>% + ungroup() %>% + mutate(group = str_remove_all(group, "\\*"), + mean = round(mean, digits = 4), + sd = round(sd, digits = 4), + cv = round(cv, digits = 4)) %>% + select(Pigment = pigment_label, `Genotype and Tissue` = group, Mean = mean, SD = sd, N = n) %>% + mutate(`Genotype and Tissue` = as_factor(`Genotype and Tissue`), + `Genotype and Tissue` = fct_relevel(`Genotype and Tissue`, c("gl M82", "gl canal-1", "wl canal-1"))) %>% + arrange(Pigment, `Genotype and Tissue`) + +write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Leaf_pigments_sum_stat.xlsx") + + +# stems pigment plot ----------------------------------------------------- + +chl_plot_stems <- chl_plot %>% + filter(pigment != "ratio_chla_chlb", + tissue_group == "stems") %>% + mutate(group = group_anova) %>% + left_join(pigments) + +tissues <- chl_plot_stems %>% + distinct(group, Tissue) + +y_for_p <- chl_plot_stems %>% + group_by(pigment) %>% + summarise(y.position = 1.1*max(concentration)) + +sig_stems_plot <- sig_stems %>% + left_join(tissues, by = c("group2" = "group")) %>% + mutate(p.signif = if_else(p.value <= 0.0005, "***", + if_else(p.value <= 0.005, "**", + if_else(p.value <= 0.05, "*", "ns")))) %>% + left_join(y_for_p, by = c("var" = "pigment")) %>% + left_join(pigments, by = c("var" = "pigment")) %>% + filter(var != "ratio_chla_chlb") %>% + mutate(group = group1) + +binwidth <- chl_plot_stems %>% + summarise(binwidth = (max(concentration) - min(concentration))/100) %>% + ungroup() + +chl_plot_stems %>% + ggplot(aes(x = group, y = concentration, fill = group)) + + geom_boxplot(size = 0.2, outlier.size = NA, outlier.shape = NA) + + #geom_beeswarm(aes(x = group, y = concentration, fill = group), + # shape = 21) + + geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) + + stat_pvalue_manual(sig_stems_plot, y.position = "y.position", + label = "p.signif", step.increase = 0.07, + hide.ns = T) + + facet_grid(cols = vars(pigment_label), scales = "free") + + scale_fill_manual(values = c("darkgreen", "#a0c45a63")) + + labs(x = "Tissue and genotype", y = "concentration/ µg/g FW") + + com_theme + +ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_stem_pigments.jpg"), + width = 183, + height = 100, + units = "mm", + dpi = 300) + +saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_stem_pigments.RDS") + +sum_stat <- chl_plot_stems %>% + group_by(group, pigment_label) %>% + summarise(mean = mean(concentration), + sd = sd(concentration), + cv = sd/mean, + n = n()) %>% + ungroup() %>% + mutate(group = str_remove_all(group, "\\*"), + mean = round(mean, digits = 4), + sd = round(sd, digits = 4), + cv = round(cv, digits = 4)) %>% + select(Pigment = pigment_label, `Genotype and Tissue` = group, Mean = mean, SD = sd, N = n) %>% + mutate(`Genotype and Tissue` = as_factor(`Genotype and Tissue`), + `Genotype and Tissue` = fct_relevel(`Genotype and Tissue`, c("st M82", "st canal-1"))) %>% + arrange(Pigment, `Genotype and Tissue`) + +write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Stems_pigments_sum_stat.xlsx") + + +# Comparison pigments apolar LC ------------------------------------------- + +pig_LC <- read_csv("pigments_apolar_LC.csv") %>% + left_join(plot_label) %>% + mutate(tissue_group = as_factor(str_extract(tissue, "(?<!\\w)\\w+$")), + tissue_short = abbreviate(tissue, minlength = 2), + group_anova = as_factor(str_c(tissue_short,group, sep =" "))) + +pigments_LC <- tibble( + Chlorophyll_a = "chlorophyll a", + Chlorophyll_b = "chlorophyll b", + car_tot_ug_g = "total carotenoids", + ratio_chla_chlb = "chlorophyll a/b ratio") %>% + pivot_longer(cols = everything(), + names_to = "Compound_Name", + values_to = "pigment_label") + +chl_calc_LC <- pig_LC %>% + filter(str_detect(Compound_Name, "Chlorophyll"), + tissue_group == "leaves" | tissue_group == "stems", + year == "2018" | year == "2019") %>% + mutate(fw_norm = area/fw_in_mg) %>% + select(Compound_Name, genotype, Tissue = tissue, aliquot_ID, tissue_group, fw_norm, group_anova) %>% + left_join(pigments_LC) %>% + pivot_wider(id_cols = c(genotype, Tissue, aliquot_ID, tissue_group, group_anova), + names_from = pigment_label, + values_from = fw_norm) + +levels(chl_calc_LC$group_anova) + +chl_calc_LC$group_anova <- fct_relevel(chl_calc_LC$group_anova, + c("gl M82", + "gl *canal-1*", + "wl *canal-1*", + "st M82", + "st *canal-1*")) + +chl_calc_LC %>% + filter(tissue_group == "stems") %>% + ggplot(aes(x = Tissue, y = `chlorophyll a`, fill = genotype)) + + geom_dotplot(binaxis = "y", stackdir = "center", position = "dodge") + +chl_plot_LC <- chl_calc_LC %>% + pivot_longer(cols = c(contains("chloro")), + names_to = "pigment", + values_to = "concentration") %>% + mutate(pigment = as_factor(pigment)) + +sum_stats_LC <- chl_plot_LC %>% + group_by(pigment, Tissue, genotype) %>% + summarise(norm_dist = shapiro.test(concentration)$p.value, + mean = mean(concentration), + sd = sd(concentration), + n = n()) %>% + mutate(se = sd/sqrt(n)) + +data_leaves_LC <- chl_calc_LC %>% + filter(tissue_group == "leaves") %>% + select(group_anova, `chlorophyll a`, `chlorophyll b`) + +numeric_data_leaves <- data_leaves %>% + select(where(is.numeric)) + +map_dfc(.x = numeric_data_leaves, .f = ~leveneTest(.x ~ data_leaves$group_anova)) +map_dfc(.x = numeric_data_leaves, .f = ~kruskal.test(.x ~ data_leaves$group_anova)$p.value) +leaves_wilcox_LC <- map(.x = numeric_data_leaves, .f = ~pairwise.wilcox.test(.x, data_leaves_LC$group_anova)) + +sig_leaves_LC <- leaves_wilcox_LC %>% + map(.f = tidy) %>% + map2(.y = names(leaves_wilcox), .f = ~.x %>% mutate(var = .y)) %>% + purrr::reduce(bind_rows) + +data_stems_LC <- chl_calc_LC %>% + filter(tissue_group == "stems") %>% + select(group_anova, `chlorophyll a`, `chlorophyll b`) + +numeric_data_stems_LC <- data_stems_LC %>% + select(where(is.numeric)) + +map_dfc(.x = numeric_data_stems_LC, .f = ~leveneTest(.x ~ data_stems_LC$group_anova)) +map_dfc(.x = numeric_data_stems_LC, .f = ~kruskal.test(.x ~ data_stems_LC$group_anova)$p.value) +stems_t.test <- map(.x = numeric_data_stems_LC, .f = ~pairwise.t.test(.x, data_stems_LC$group_anova)) + +sig_stems <- stems_t.test %>% + map(.f = tidy) %>% + map2(.y = names(stems_t.test), .f = ~.x %>% mutate(var = .y)) %>% + purrr::reduce(bind_rows) + +#single tests +#leveneTest(chla_ug_g ~ group_anova, data = data_chl_a_leaves) +#kruskal.test(chla_ug_g ~ group_anova, data = data_chl_a_leaves)$p.value +#pairwise.wilcox.test(data_chl_a_leaves$chla_ug_g, +# data_chl_a_leaves$group_anova) + + +# Scale and Combine both datasets --------------------------------------------------- + + +chl_plot_leaves_LC <- chl_plot_LC %>% + filter(pigment != "ratio_chla_chlb", + tissue_group == "leaves") %>% + mutate(group = group_anova) %>% + rename(pigment_label = pigment) + +chl_leaves_phot <- chl_plot_leaves %>% + filter(!str_detect(pigment_label, "carotenoid")) %>% + select(genotype, Tissue, aliquot_ID = `Aliquot ID`, tissue_group, group_anova, pigment_label, concentration) %>% + mutate(method = "Photometer") + +chl_leaves_LC <- chl_plot_leaves_LC %>% + mutate(method = "apolar LC") + +id_in_both <- chl_leaves_phot %>% + full_join(chl_leaves_LC) %>% + distinct(group_anova, aliquot_ID, method) %>% + group_by(group_anova, aliquot_ID) %>% + summarise(n = n()) %>% + ungroup() + +chl_leaves_comb <- chl_leaves_phot %>% + bind_rows(chl_leaves_LC) %>% + left_join(id_in_both) %>% + filter(n == 2) + +wt_leaves <- chl_leaves_comb %>% + filter(group_anova == "gl M82") %>% + group_by(pigment_label, method) %>% + summarise(wt_mean = mean(concentration)) + +chl_leaves_scaled <- chl_leaves_comb %>% + left_join(wt_leaves) %>% + mutate(level = concentration/wt_mean) + +chl_leaves_scaled %>% + ggplot(aes(x = group_anova, y = level, fill = method)) + + geom_boxplot() + + geom_point(shape = 21, position = position_dodge(0.75)) + + facet_wrap(facets = vars(pigment_label)) + + com_theme + + theme(legend.position = "bottom") + +ggsave("comparison_chlorophyll.jpg", width = 16, height = 20, units = "cm") + +chl_leaves_scaled %>% + ggplot(aes(x = method, y = level, fill = method)) + + geom_boxplot() + + geom_point(shape = 21, position = position_dodge(0.75)) + + geom_line(aes(x = method, y = level, group = aliquot_ID)) + + facet_grid(cols = vars(pigment_label), rows = vars(group_anova), scales = "free") + +ggsave("comparison_chlorophyll_ids_outlier.jpg", width = 16, height = 20, units = "cm") + +chl_leaves_scaled %>% + filter(aliquot_ID != "2131611") %>% + ggplot(aes(x = method, y = level, fill = method)) + + geom_boxplot() + + geom_point(shape = 21, position = position_dodge(0.75)) + + geom_line(aes(x = method, y = level, group = aliquot_ID)) + + facet_grid(cols = vars(pigment_label), rows = vars(group_anova), scales = "free") + +ggsave("comparison_chlorophyll_ids_outlier_removed.jpg", width = 16, height = 20, units = "cm")