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scripts/plotting/LINEAGE2.R
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251
scripts/plotting/LINEAGE2.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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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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cols_to_subset = c('mutationinformation'
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, 'snp_frequency'
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, 'pos_count'
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, 'position'
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, 'lineage'
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, 'lineage_multimode'
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, 'dst'
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, 'dst_multimode'
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#, 'dst_multimode_all'
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, 'dst_mode')
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my_df = df[ ,cols_to_subset]
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#df2 = df2[ ,cols_to_subset]
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r24p_embb = df_embb[df_embb$mutationinformation == "R24P",]
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tm = c("A102P", "M1T")
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test = my_df[my_df$mutationinformation%in%tm,]
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#test$dst2[is.na(test$dst)] <-test$dst_mode
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test$dst2 = ifelse(is.na(test$dst), test$dst_mode, test$dst)
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sum(table(test$dst2)) == nrow(test)
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# Now we need to make a column that fill na in dst with value of dst_mode
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my_df$dst2 = ifelse(is.na(my_df$dst), my_df$dst_mode, my_df$dst)
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#%% create sensitivity column ~ dst_mode
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my_df$sensitivity = ifelse(my_df$dst2 == 1, "R", "S")
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table(my_df$dst2)
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if ( table(my_df$sensitivity)[2] == table(my_df$dst2)[1] && table(my_df$sensitivity)[1] == table(my_df$dst2)[2] ){
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cat("\nProceeding with lineage resistance plots")
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}else{
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stop("FAIL: could not verify dst2 and sensitivity numbers")
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}
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#%%
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# select only L1-L4 and LBOV
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sel_lineages1 = c("LBOV", "")
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my_df2 = my_df[!my_df$lineage%in%sel_lineages1,]
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table(my_df2$lineage)
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sel_lineages2 = c("L1", "L2", "L3", "L4")
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my_df2 = my_df2[my_df2$lineage%in%sel_lineages2,]
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table(my_df2$lineage)
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sum(table(my_df2$lineage)) == nrow(my_df2)
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table(my_df2$lineage)
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# %%
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# str(my_df2)
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# my_df2$lineage = as.factor(my_df2$lineage)
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# my_df2$sensitivity = as.factor(my_df2$sensitivity)
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#%% get only muts which belong to > 1 lineage and have different sensitivity classifications
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muts = unique(my_df2$mutationinformation)
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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_df2[my_df2$mutationinformation == i,]
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s_tab = table(s_mut$lineage, s_mut$sensitivity)
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#print(s_tab)
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if (dim(s_tab)[1] > 1 && dim(s_tab)[2] > 1){
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lin_muts = c(lin_muts, i)
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}
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}
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cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities")
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# step2: subset these muts for plotting
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plot_df = my_df2[my_df2$mutationinformation%in%lin_muts,]
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cat("\nnrow of plot_df:", nrow(plot_df))
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# Add p-value
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plot_df$pval = NULL
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for (i in lin_muts) {
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print (i)
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s_mut = plot_df[plot_df$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)$p.value, 2)
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print(ft_pvalue_i)
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# #my_df[my_df['mutationinformation']==i,]['ft_pvalue']= ft_pvalue_i
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#plot_df[plot_df['mutationinformation']==i,]['p.value']= ft_pvalue_i
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plot_df$pval[plot_df$mutationinformation == i] <- ft_pvalue_i
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#print(s_tab)
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}
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head(plot_df$pval)
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#plot_df$ypos_label = plot_df$snp_frequency+0.8
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#======================================
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# Plot attempt 1: 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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######################
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# geom_rect
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ggplot(test2, aes(x = lineage
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, fill = factor(sensitivity))) +
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ggplot() +
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geom_rect(data = plot_df
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, aes(xmin = as.numeric( length(unique(lineage)) ) - 4
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, ymax = as.numeric( ypos_label ) + 1
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, xmax = as.numeric( length(unique(lineage)) )
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, ymin = as.numeric( (min(ypos_label)-min(ypos_label))) - 0.5
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))+
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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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#%% Plot attempt 2
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# quick test
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tm2 = c("F94L", "A102P", "L4S")
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#tm2 = c("F94L")
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# Calculate stats: example
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test2 = plot_df[plot_df$mutationinformation%in%tm2,]
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table(test2$mutationinformation, test2$lineage, test2$sensitivity)
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tm_tab = table(test2$lineage, test2$sensitivity)
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tm_tab
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fisher.test(tm_tab)
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chisq.test(tm_tab)
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#--------------------------------------------
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# Plot test: 1 graph with fisher test stats
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# precalculated
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#-------------------------------------------
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ggplot(test2, aes(x = lineage
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#, y = snp_frequency
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, fill = factor(sensitivity))) +
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geom_bar(stat = 'count') +
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#geom_bar(stat = "identity")+
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facet_wrap(~mutationinformation
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, scales = 'free_y') +
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#geom_text(aes(label = p.value, x = 0.5, y = 5))
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geom_label(aes(label = pval, x = 0.5, ypos_label))
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##############################
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ggplot(test2, aes(x = lineage
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, y = stat(count/sum(count))
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, fill = factor(sensitivity))) +
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geom_bar(stat = 'count') +
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#geom_bar(stat = 'identity') +
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facet_wrap(~mutationinformation
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, scales = 'free_y') +
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# geom_signif(comparisons = list(c("L2", "L3", "L4"))
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# , test = "fisher.test"
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# , position = 'identity') +
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geom_label(aes(label = p.value, vjust = 0))
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tm_tab_df = as.data.frame(tm_tab)
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tm_tab_df
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class(tm_tab_df)
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colnames(tm_tab_df) = c("lineage", "sensitivity", "var_count")
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tm_tab_df
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fisher.test(tm_tab)
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ggplot(tm_tab_df, aes(x = lineage
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, y = var_count
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, fill = sensitivity)) +
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geom_bar(stat = "identity") +
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geom_signif(comparisons = list(c("L2", "L3", "L4"))
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, test = "fisher.test"
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#, y = stat(count/sum(count))
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)
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#geom_signif(data = tm_tab_df, test = "fisher.test", map_signif_level = function(p) sprintf("p = %.2g", p) )
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# try
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test2 %>%
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group_by(mutationinformation) %>%
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count(lineage) %>%
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#mutate(p_val = pval/1) %>%
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#count(sensitivity, pval) %>%
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#mutate(Freq = n / sum(n)) %>%
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mutate(ypos_label = max(n))
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ggplot() +
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#aes(lineage, Freq, fill = sensitivity) +
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aes(lineage, n, fill = sensitivity) +
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geom_bar(stat = "identity") +
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#geom_label(aes(label = pval, vjust = 0), x = 0.5, y = 5)
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geom_signif(comparisons = list(c("L1", "L2", "L3", "L4"), na.rm = TRUE)
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, test = "fisher.test")
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# get the X and y coordinates for label
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lin_muts_tb = test2 %>%
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group_by(mutationinformation) %>%
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count(lineage) %>%
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#mutate(p_val = pval/1) %>%
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#count(sensitivity, pval) %>%
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#mutate(Freq = n / sum(n)) %>%
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mutate(ypos_label = max(n))
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head(lin_muts_tb)
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class(lin_muts_tb)
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lin_muts_df = as.data.frame(lin_muts_tb)
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class(lin_muts_df)
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intersect(names(test2), names(lin_muts_df))
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sub_cols = c("mutationinformation", "ypos_label")
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lin_muts_df2 = lin_muts_df[, sub_cols]
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names(lin_muts_df2)
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lin_muts_df2U = lin_muts_df2[!duplicated(lin_muts_df2),]
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class(lin_muts_df2); class(test2); class(lin_muts_df2U)
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lin_muts_dfM = merge(test2, lin_muts_df2U, by = "mutationinformation", all.y = T)
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if nrow(lin_muts_dfM) == nrow(test2)
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# now plot
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ggplot(lin_muts_dfM, aes(x = lineage
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#, y = snp_frequency
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, fill = factor(sensitivity))) +
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geom_bar(stat = 'count') +
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#geom_bar(stat = "identity")+
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facet_wrap(~mutationinformation
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, scales = 'free_y') +
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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=",pval), x = 2.5, ypos_label+1), fill="white")# +
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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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#geom_bracket(data=lin_muts_dfM, aes(xmin = 1, xmax = 4, y.position = ypos_label+0.5, label=''))
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