modified bp with option for adding stats and boxplplots. Moved old one to redundant
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8 changed files with 443 additions and 102 deletions
97
scripts/functions/redundant/lf_bp_with_stats.R
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97
scripts/functions/redundant/lf_bp_with_stats.R
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library(ggpubr)
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###################################################################
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####################################
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lf_bp_with_stats <- function(lf_df
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, x_grp = "mutation_info"
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, y_var = "param_value"
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, facet_var = "param_type"
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, n_facet_row = 1
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, y_scales = "free_y"
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, p_title = ""
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, colour_categ = ""
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, colour_bp_strip = "khaki2"
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, bp_width = c("auto", 0.5)
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, dot_size = 3
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, dot_transparency = 0.3
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, stat_label = c("p.format", "p.signif")
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size
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) {
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if (bp_width == "auto"){
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bp_width = 0.5/length(unique(lf_df[[x_grp]]))
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cat("\nAutomatically calculated boxplot width, using bp_width:\n", bp_width, "\n")
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}else{
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cat("\nBoxplot width value provided, using:", bp_width, "\n")
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bp_width = bp_width
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}
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my_comparisonsL <- list( stat_grp_comp )
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bp_statP <- ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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, y = eval(parse(text = y_var)) )) +
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facet_wrap(~ eval(parse(text = facet_var))
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, nrow = n_facet_row
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, scales = y_scales) +
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geom_violin(trim = T
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, scale = "width"
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#, position = position_dodge(width = 0.9)
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, draw_quantiles = c(0.25, 0.5, 0.75)) +
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# geom_boxplot(fill = "white"
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# , outlier.colour = NA
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# #, position = position_dodge(width = 0.9)
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# , width = bp_width) +
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# geom_point(position = position_jitterdodge(dodge.width = 0.5)
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# , alpha = 0.5
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# , show.legend = FALSE
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# , aes(colour = factor(eval(parse(text = colour_categ))) )) +
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# ggbeeswarm (better than geom_point)
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geom_beeswarm(priority = "density"
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#, shape = 21
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ))) )) +
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts
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, hjust = 0.5
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, colour = "black"
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, face = "bold")
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, strip.background = element_rect(fill = colour_bp_strip)
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, strip.text.x = element_text(size = my_fls
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, colour = "black")
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, legend.title = element_text(color = "black"
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, size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical") +
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labs(title = p_title
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, x = ""
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, y = "")+
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stat_compare_means(comparisons = my_comparisonsL
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, method = stat_method
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, paired = my_paired
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, label = stat_label[1])
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return(bp_statP)
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}
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83
scripts/functions/redundant/test_lf_bp_with_stats.R
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scripts/functions/redundant/test_lf_bp_with_stats.R
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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source("../functions/lf_bp_with_stats.R")
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source("../functions/lf_bp.R")
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######################
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# Make plot
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######################
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# Note: Data
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# run other_plots_data.R
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# to get the long format data to test this function
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lf_bp(lf_df = lf_dynamut2
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, p_title = "Dynamut2"
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, colour_categ = "ddg_dynamut2_outcome"
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, x_grp = "mutation_info"
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, y_var = "param_value"
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, facet_var = "param_type"
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75)
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size
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, make_boxplot = F
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, bp_width = "auto"
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, add_stats = T
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, stat_label = c("p.format", "p.signif") )
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# foo = lf_dynamut2 %>%
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# group_by(mutation_info, param_type) %>%
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# summarise( Mean = mean(param_value, na.rm = T)
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# , SD = sd(param_value, na.rm = T)
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# , Median = median(param_value, na.rm = T)
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# , IQR = IQR(param_value, na.rm = T) )
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# Quick tests
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plotdata_sel = subset(lf_dynamut2
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, lf_dynamut2$param_type == "ASA")
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plot_sum = plotdata_sel %>%
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group_by(mutation_info, param_type) %>%
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summarise(n = n()
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, Mean = mean(param_value, na.rm = T)
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, SD = sd(param_value, na.rm = T)
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, Min = min(param_value, na.rm = T)
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, Q1 = quantile(param_value, na.rm = T, 0.25)
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, Median = median(param_value, na.rm = T)
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, Q3 = quantile(param_value, na.rm = T, 0.75)
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, Max = max(param_value, na.rm = T) ) %>%
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rename('Mutation Class' = mutation_info
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, Parameter = param_type)
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plot_sum = as.data.frame(plot_sum, row.names = NULL)
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plot_sum
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bar = compare_means(param_value ~ mutation_info
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, group.by = "param_type"
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, data = plotdata_sel
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, paired = FALSE
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, p.adjust.method = "BH")
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bar2 = bar[c("param_type"
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, "group1"
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, "group2"
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, "p.format"
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, "p.signif"
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, "p.adj")] %>%
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rename(Parameter = param_type
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, Group1 = group1
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, Group2 = group2
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, "P-value" = p.format
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, "P-sig" = p.signif
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, "P-adj" = p.adj)
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bar2 = data.frame(bar2); bar2
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library(Hmisc)
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describe(lf_dynamut2)
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