horrible lineage analysis hell
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325
scripts/plotting/LINEAGE.R
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325
scripts/plotting/LINEAGE.R
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library(tidyverse)
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#install.packages("ggforce")
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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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#%% read data
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df = read.csv("/home/tanu/git/Data/pyrazinamide/output/pnca_merged_df2.csv")
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#df = 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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my_df = df[ ,c('mutationinformation'
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, 'snp_frequency'
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, 'pos_count'
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, 'lineage'
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, 'lineage_multimode'
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, 'dst'
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, 'dst_mode')]
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#%% create sensitivity column ~ dst_mode
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my_df$sensitivity = ifelse(my_df$dst_mode == 1, "R", "S")
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table(my_df$dst_mode)
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table(my_df$sensitivity)
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test = my_df[my_df$mutationinformation=="A102P",]
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# fix the lineage_multimode labels
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my_df$lineage_multimode
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my_df$lineage_mm <- gsub("\\.0", "", my_df$lineage_multimode)
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my_df$lineage_mm
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my_df$lineage_mm <- gsub("\\[|||]", "", my_df$lineage_mm)
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str(my_df$lineage_mm)
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table(my_df$lineage_mm)
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my_dfF = separate_rows(my_df, lineage_mm, sep = ",")
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my_dfF = as.data.frame(my_dfF)
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table(my_dfF$lineage_mm)
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my_dfF$lineage_mm <- gsub(" ", "", my_dfF$lineage_mm)
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table(my_dfF$lineage_mm)
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# addd prefix L
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my_dfF$lineage_mm = paste0("L", my_dfF$lineage_mm)
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table(my_dfF$lineage_mm)
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if (class(my_df) == class(my_dfF)){
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cat('\nPASS: separated lineage multimode label column')
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my_df = my_dfF
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} else{
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cat('\nFAIL: could not split lineage multimode column')
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}
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# select only L1-L4 and LBOV
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sel_lineages = c("L1", "L2", "L3", "L4")
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table(my_df$lineage_mm)
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my_df2 = my_df[my_df$lineage_mm%in%sel_lineages,]
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table(my_df2$lineage)
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sum(table(my_df2$lineage_mm)) == nrow(my_df2)
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dup_rows = my_df2[duplicated(my_df2[c('mutationinformation')]), ]
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expected_nrows = nrow(my_df2) - nrow(dup_rows)
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my_df3 = my_df2[!duplicated(my_df2[c('mutationinformation')]), ]
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if ( nrow(my_df3) == expected_nrows ) {
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cat('\nPASS: duplicated rows removed')
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}else{
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cat('\nFAIL: duplicated rows could not be removed')
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}
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table(my_df3$lineage_mm)
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str(my_df3$lineage_mm)
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# convert to factor
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str(my_df3)
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my_df3$lineage = as.factor(my_df3$lineage)
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my_df3$lineage_mm = as.factor(my_df3$lineage_mm)
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my_df3$sensitivity = as.factor(my_df3$sensitivity)
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str(my_df3$lineage_mm)
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#df2 = my_df2[1:100,]
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df2 = my_df3
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sum(table(df2$mutationinformation))
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table(df2$lineage_mm)
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str(df2$lineage_mm)
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#df3 = df2[na.omit(df2$dst)]
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#sum(is.na(df2$dst))
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df3 = df2[!is.na(df2$dst), ]
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nrow(df3)
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#%% plot
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#============
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# facet wrap
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#============
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plot_data = df2
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plot_data = df3
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table(plot_data$mutationinformation, plot_data$lineage_mm, plot_data$dst)
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test2 = my_df[1:500, ]
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test2 = my_df
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test2 = test2[test2$lineage%in%sel_lineages,]
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nrow(test2)
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# stats
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f2 = test2[test2$mutationinformation == "Y95D",]
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h = table(f2$lineage, f2$dst); h
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h2 = table(f2$lineage, f2$dst_mode); h2
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length(h)
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length(h2)
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f2 = test2[test2$mutationinformation == "Y95D",]
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h = table(f2$lineage, f2$dst); h
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h2 = table(f2$lineage, f2$sensitivity); h2
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length(h)
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length(h2)
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tm = "G97A" # 1
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tm = "L117R"
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tm = "D63G"
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tm = "A102P"
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tm = "F13L"
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tm = "E174G"
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tm = "L182S"
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tm = "L4S"
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f3 = test2[test2$mutationinformation == tm,]
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h3 = table(f3$lineage, f3$sensitivity); h3
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print(h3)
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print(class(h3))
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print(dim(h3))
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dim(h3)[1] # >1
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dim(h3)[2] #>1
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#h3 = table(f3$lineage); h3
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length(h3)
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h3v2 = table(f3$lineage, f3$sensitivity); h3v2
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length(h3v2)
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#if length is > 2, then get these
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chisq.test(h3)
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chisq.test(h3)$p.value
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#ggchisqtest(chisq.test(h3))
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fisher.test(h3)
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fisher.test(h3)$p.value
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#########################
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muts = unique(my_df2$mutationinformation)
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my_df = my_df2
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# step1 : get muts with more than one lineage
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lin_muts = NULL
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for (i in muts) {
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print (i)
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s_mut = my_df[my_df$mutationinformation == i,]
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s_tab = table(s_mut$lineage, s_mut$sensitivity)
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#s_tab = table(s_mut$lineage)
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#print(s_tab)
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#if (length(s_tab) > 1 ){
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# if (dim(s_tab)[1] > 1 ){
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# lin_muts = c(lin_muts, i)
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if (dim(s_tab)[1] > 1 && dim(s_tab)[2] > 1){
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lin_muts = c(lin_muts, i)
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}
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}
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# # now from the above list, get only the ones that have both R and S
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# muts_var = NULL
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# for (i in lin_muts) {
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# print (i)
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# s_mut = my_df[my_df$mutationinformation == i,]
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# s_tab = table(s_mut$lineage, s_mut$sensitivity)
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# print(s_tab)
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# print(dim(s_tab)[2]) # if this is one, we are uninterested
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# if ( dim(s_tab)[2] > 1 ){
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# muts_var = c(muts_var, i)
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# }
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# }
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# now final check
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for (i in lin_muts) {
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print (i)
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s_mut = my_df[my_df$mutationinformation == i,]
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s_tab = table(s_mut$lineage, s_mut$sensitivity)
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print(s_tab)
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print(c(i, "FT:", fisher.test(s_tab)$p.value))
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# print(dim(s_tab)[2]) # if this is one, we are uninterested
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# if ( dim(s_tab)[2] > 1 ){
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# muts_var = c(muts_var, i)
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# }
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}
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plot_df = my_df[my_df$mutationinformation%in%lin_muts,]
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#plot_df2 = plot_df[plot_df$lineage%in%sel_lineages,]
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table(plot_df$lineage)
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length(unique(plot_df2$mutationinformation)) == length(lin_muts)
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#muts_var
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lin_mutsL = plot_df$mutationinformation[plot_df$mutationinformation%in%lin_muts]
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plot_df$p.value = NULL
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for (i in lin_muts) {
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print (i)
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s_mut = plot_df[plot_df$mutationinformation == i,]
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print(s_mut)
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s_tab = table(s_mut$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, 2)
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print(ft_pvalue_i)
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# #my_df[my_df['mutationinformation']==i,]['ft_pvalue']= ft_pvalue_i
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#plot_df[plot_df['mutationinformation']==i,]['p.value']= ft_pvalue_i
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plot_df$p.value[plot_df$mutationinformation == i] <- ft_pvalue_i
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#print(s_tab)
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}
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plot_df2 = my_df[my_df$mutationinformation == c("A102P"),]
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#https://stackoverflow.com/questions/72618364/how-to-use-geom-signif-from-ggpubr-with-a-chi-square-test
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#########################
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library(grid)
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#sp2 + annotation_custom(grob)+facet_wrap(~cyl, scales="free")
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grob <- grobTree(textGrob("Scatter plot", x=0.1, y=0.95, hjust=0,
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gp=gpar(col="red", fontsize=5, fontface="italic")))
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#############
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chi.test <- function(a, b) {
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return(chisq.test(cbind(a, b)))
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}
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ggplot(plot_df, aes(x = lineage
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#, y = snp_frequency
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, fill = factor(sensitivity))) +
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geom_bar(
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stat = 'count'
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#stat = 'identity'
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, position = 'dodge') +
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facet_wrap(~mutationinformation
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, scales = 'free_y') +
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#coord_flip() +
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stat_count(aes(y=..count../sum(..count..), label=p.value), geom="text", hjust=0)
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#geom_text(aes(label = p.value, x = -0.5, y = 1))
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#geom_text(data = data.frame(lineage = c("L1", "L2", "L3", "L4"), p.value = "p.value" ))
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#geom_text(aes(label = p.value), stat = "count")
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#geom_text(aes(label=after_stat(count)), vjust=0, stat = "count") # shows numbers
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#geom_signif(comparisons = list(c("L1", "L2", "L3", "L4")), test = "fisher.test", y = 1)
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# geom_signif(data = data.frame(lineage = c("L1", "L2", "L3", "L4"),sensitivity = c("R", "S") )
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# , test = "fisher.test" )
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# , aes(y_position=c(5.3, 8.3), xmin=c(0.8, 0.8), xmax=c(1.2, 1.2))
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# )
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#geom_label(p.value)
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#coord_flip()
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# ggforce::facet_wrap_paginate(~mutationinformation
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# , ncol = 5
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# , nrow = 5
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# , page = 10
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# )
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# with coord flip
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ggplot(plot_data, aes(x = lineage_mm, fill = sensitivity)) +
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geom_bar(position = 'dodge') +
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facet_wrap(~mutationinformation) + coord_flip()
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#============
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# facet grid
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#============
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ggplot(plot_data, aes(x = mutationinformation, fill = sensitivity)) +
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geom_bar(position = 'dodge') +
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facet_grid(~lineage_mm)
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# with coord flip
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ggplot(plot_data, aes(x = mutationinformation, fill = sensitivity)) +
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geom_bar(position = 'dodge') +
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facet_grid(~lineage_mm)+ coord_flip()
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##########################################
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#%% useful info
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# https://stackoverflow.com/questions/13773770/split-comma-separated-strings-in-a-column-into-separate-rows
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bardf = as.data.frame(bar)
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class(bardf) == class(my_df)
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baz = my_df
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baz = baz %>%
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mutate(col2 = strsplit(as.character(col2), ",")) %>%
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unnest(col2)
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baz = as.data.frame(baz)
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class(baz) == class(bar)
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