LINEAGE2 plots: p value stars
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1 changed files with 75 additions and 29 deletions
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@ -4,15 +4,20 @@ library("ggforce")
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#install.packages("gginference")
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library(gginference)
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library(ggpubr)
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library(svglite)
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##################################################
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#%% read data
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# TODO: read data using gene and drug combination
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# DOME: read data using gene and drug combination
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# gene must be lowercase
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# tolower(gene)
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#################################################
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gene="pncA"
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drug="pyrazinamide"
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lineage_filename=paste0(tolower(gene),"_merged_df2.csv")
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lineage_data_path="~/git/Data/pyrazinamide/output"
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df = read.csv("/home/tanu/git/Data/pyrazinamide/output/pnca_merged_df2.csv")
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df = read.csv(paste0(lineage_data_path,"/",lineage_filename))
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#df2 = read.csv("/home/tanu/git/Data/pyrazinamide/output/pnca_merged_df3.csv")
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foo = as.data.frame(colnames(df))
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@ -96,33 +101,50 @@ cat("\nnrow of plot_df:", nrow(plot_df))
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#-----------------------------------------------
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plot_df$pval = NULL
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for (i in lin_muts) {
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print (i)
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#print (i)
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s_mut = plot_df[plot_df$mutationinformation == i,]
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print(s_mut)
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#print(s_mut)
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s_tab = table(s_mut$lineage, s_mut$sensitivity)
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print(s_tab)
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ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3)
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print(ft_pvalue_i)
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#print(s_tab)
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#ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3)
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ft_pvalue_i = fisher.test(s_tab)$p.value
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#print(ft_pvalue_i)
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plot_df$pval[plot_df$mutationinformation == i] <- ft_pvalue_i
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#print(s_tab)
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}
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plot_df$pvalR = round(plot_df$pval, 3)
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plot_df$pvalRF = ifelse(plot_df$pvalR == 0.05, paste0("p=",plot_df$pvalR, "."), plot_df$pvalR )
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plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.05, paste0("p=",plot_df$pvalR, "*"), plot_df$pvalRF )
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plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.01, paste0("p=",plot_df$pvalR, "**"), plot_df$pvalRF )
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plot_df$pvalRF = ifelse(plot_df$pvalR == 0, 'p<0.001, ***', plot_df$pvalRF)
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head(plot_df$pval)
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head(plot_df$pvalR)
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View(plot_df$pvalRF)
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# format p value
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# TODO: add case statement for correct pvalue formatting
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plot_df$pvalF = ifelse(plot_df$pval < 0.05, paste0(plot_df$pval, "*"), plot_df$pval )
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plot_df$pvalF
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#plot_df$pvalF = ifelse(plot_df$pval <= 0.0001, paste0(round(plot_df$pval, 3), "**** "), plot_df$pval )
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# plot_df$pvalF = ifelse(plot_df$pval <= 0.001, paste0(round(plot_df$pval, 3), "*** "), plot_df$pval )
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# plot_df$pvalF = ifelse(plot_df$pval <= 0.01, paste0(round(plot_df$pval, 3), "** "), plot_df$pval )
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# plot_df$pvalF = ifelse(plot_df$pval < 0.05, paste0(round(plot_df$pval, 3), "* "), plot_df$pval )
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# plot_df$pvalF = ifelse(plot_df$pval == 0.05, paste0(round(plot_df$pval, 3), ". "), plot_df$pval )
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round(plot_df$pvalF, 3)
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#================================================
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# Plot attempt 1 [no stats]: WORKS beeautifully
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#================================================
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ggplot(plot_df, aes(x = lineage
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, fill = factor(sensitivity))) +
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geom_bar(stat = 'count')+
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#coord_cartesian(ylim = c(0, ypos_label)) +
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facet_wrap(~mutationinformation
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, scales = 'free_y')
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# ggplot(plot_df, aes(x = lineage
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# , fill = factor(sensitivity))) +
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# geom_bar(stat = 'count')+
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# #coord_cartesian(ylim = c(0, ypos_label)) +
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# facet_wrap(~mutationinformation
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# , scales = 'free_y')
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#########################################################
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#================================================
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@ -175,23 +197,47 @@ if (nrow(lin_muts_dfM) == nrow(plot_df) ){
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#================================================
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# Plot: with stats (plot_df2)
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# TODO:
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#1) Add gene name from variable as plot title. <Placeholder provided>
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#2) Add: facet_wrap_paginate () to allow graphs to span over multiple pages
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#1) DONE: Add gene name from variable as plot title. <Placeholder provided>
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#2) DONE: Add: facet_wrap_paginate () to allow graphs to span over multiple pages
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#3) Add *: Extend yaxis for each plot to allow geom_label to have space (or see
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# if this self resolving with facet_wrap_paginate())
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#================================================
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p_title = "<Insert gene>"
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plot_pages = round(length(lin_muts)/25)
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p_title = gene
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res = 144 # SVG dots-per-inch
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ggplot(plot_df2, aes(x = lineage
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, fill = sensitivity)) +
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geom_bar(stat = 'count') +
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facet_wrap(~mutationinformation
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, scales = 'free_y') +
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theme(legend.position = "top")+
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labs(title = p_title) +
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#geom_text(aes(label = p.value, x = 0.5, y = 5))
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geom_label(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1), fill="white")# +
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#geom_text(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1))# +
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sapply(1:plot_pages, function(page){
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print(paste0("Plotting page:", page))
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svglite(paste0("/tmp/output-",page,".svg"), width=2048/res, height=1534/res) # old-school square 4:3 CRT shape 1.3:1
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print(
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ggplot(plot_df2, aes(x = lineage
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, fill = sensitivity)) +
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geom_bar(stat = 'count') +
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facet_wrap_paginate(~mutationinformation
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, scales = 'free_y'
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, ncol = 5
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, nrow = 5
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, page = page) +
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theme(legend.position = "top"
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, plot.title = element_text(hjust = 0.5, size=20)
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, strip.text = element_text(size=14)
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, axis.text.x = element_text(size=14)
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, axis.text.y = element_text(size=14)
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, axis.title.y = element_text(size=14)
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, legend.title = element_blank()
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, axis.title.x = element_blank()
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)+
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labs(title = paste0(p_title, ": sensitivity by lineage")
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, y = 'Sample Count'
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) +
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#geom_text(aes(label = p.value, x = 0.5, y = 5))
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geom_blank(aes(y = ypos_label+2)) +
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geom_label(aes(label = pvalRF, x = 2.5, ypos_label+0.75), fill="white")
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)
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dev.off()
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}
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)
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#geom_text(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1))# +
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#geom_segment(aes(x = 1, y = ypos_label+0.5, xend = 4, yend = ypos_label+0.5))
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#geom_hline(data = lin_muts_dfM, aes(yintercept=ypos_label+0.5))
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