generated embb lineage plots
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3 changed files with 208 additions and 64 deletions
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@ -11,46 +11,55 @@ library(svglite)
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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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#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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#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(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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df2 = read.csv(paste0(lineage_data_path,"/",lineage_filename))
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foo = as.data.frame(colnames(df))
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foo = as.data.frame(colnames(df2))
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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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, "sensitivity"
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#, 'lineage_multimode'
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, 'dst'
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, 'dst_multimode'
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, 'dst2'
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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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#cols_to_subset%in%foo
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r24p_embb = df_embb[df_embb$mutationinformation == "R24P",]
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my_df = df2[ ,cols_to_subset]
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tm = c("A102P", "M1T")
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test = my_df[my_df$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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# 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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# already exist now
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#---------------------------------
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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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#my_df$dst2_check = ifelse(is.na(my_df$dst), my_df$dst_mode, my_df$dst)
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#all(my_df$dst2_check ==my_df$dst2)
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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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#%% create sensitivity column ~ dst2[revised]
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my_df$sens2 = ifelse(my_df$dst2 == 1, "R", "S")
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#---------------------------------
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table(my_df$sens2)
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table(my_df$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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if ( table(my_df$sens2 )[2] == table(my_df$dst2)[1] && table(my_df$sens2 )[1] == table(my_df$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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@ -72,30 +81,39 @@ 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$sensitivity)
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# my_df2$sens2 = as.factor(my_df2$sens2)
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#%% get only muts which belong to > 1 lineage and have different sensitivity classifications
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muts = unique(my_df2$mutationinformation)
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#-----------------------------------------------
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# step1 : get muts with more than one lineage
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# step 0 : get muts with more than one lineage
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#-----------------------------------------------
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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$sensitivity)
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s_tab = table(s_mut$lineage, s_mut$sens2)
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#print(s_tab)
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if (dim(s_tab)[1] > 1 && dim(s_tab)[2] > 1){
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lin_muts = c(lin_muts, i)
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}
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}
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cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities")
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#-----------------------------------------------
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# step 1 : get other muts that do not have this
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#-----------------------------------------------
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consist_muts = muts[!muts%in%lin_muts]
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cat("\nGot:", length(consist_muts), "mutations that are consistent")
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#-----------------------------------------------
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# step 2: subset these muts for plotting
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#-----------------------------------------------
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plot_df = my_df2[my_df2$mutationinformation%in%lin_muts,]
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cat("\nnrow of plot_df:", nrow(plot_df))
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#-----------------------------------------------
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# step 3: Add p-value
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#-----------------------------------------------
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@ -104,7 +122,7 @@ 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$sensitivity)
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s_tab = table(s_mut$lineage, s_mut$sens2)
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#print(s_tab)
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#ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3)
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ft_pvalue_i = fisher.test(s_tab)$p.value
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@ -114,11 +132,30 @@ for (i in lin_muts) {
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}
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plot_df$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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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 == 0.05, 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$pvalRF)
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# plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_df$pvalRF)
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# plot_df
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# plot_df$pvalRF_old = plot_df$pvalRF
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plot_df$pvalRF = plot_df$pvalR
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plot_df = dplyr::mutate(plot_df
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, pvalRF = case_when(pvalRF == 0.05 ~ "P ."
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, pvalRF <=0.0001 ~ 'P ****'
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, pvalRF <=0.001 ~ 'P ***'
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, pvalRF <=0.01 ~ 'P **'
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, pvalRF <0.05 ~ 'P *'
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, TRUE ~ 'ns'))
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plot_df
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head(plot_df)
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table(plot_df$pvalR<0.05)
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# format p value
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# TODO: add case statement for correct pvalue formatting
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@ -149,8 +186,8 @@ plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_d
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#
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# # Calculate stats: example
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# test2 = plot_df[plot_df$mutationinformation%in%tm2,]
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# table(test2$mutationinformation, test2$lineage, test2$sensitivity)
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# tm_tab = table(test2$lineage, test2$sensitivity)
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# table(test2$mutationinformation, test2$lineage, test2$sens2)
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# tm_tab = table(test2$lineage, test2$sens2)
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# tm_tab
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# Get the ypos for plotting the label for p-value
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@ -179,13 +216,26 @@ if (nrow(lin_muts_dfM) == nrow(plot_df) ){
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cat("\nPASS: plot_df now has ypos for label"
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, "\nGenerating plot_df2 with sensitivity as factor\n")
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str(lin_muts_dfM)
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lin_muts_dfM$sensitivity = as.factor(lin_muts_dfM$sensitivity)
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lin_muts_dfM$sens2 = as.factor(lin_muts_dfM$sens2)
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plot_df2 = lin_muts_dfM
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}else{
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stop("\nSomething went wrong. ypos_label could not be generated")
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}
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# sig muts
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plot_df_sig = plot_df2[plot_df2$pvalR<0.05,]
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sig_muts = length(unique(plot_df_sig$mutationinformation))
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cat("\nGot:", sig_muts, "mutations that are significant")
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plot_df_ns = plot_df2[plot_df2$pvalR>0.05,]
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ns_muts = length(unique(plot_df_ns$mutationinformation))
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cat("\nGot:", ns_muts, "mutations that are NOT significant")
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p_title = gene
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ts = 8
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gls = 3
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#================================================
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# Plot: with stats (plot_df2)
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# TODO:
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@ -194,41 +244,94 @@ if (nrow(lin_muts_dfM) == nrow(plot_df) ){
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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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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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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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#svg(paste0(outdir_images, "embb_linDS.svg"), width = 6, height = 10 ) # old-school square 4:3 CRT shape 1.3:1
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ds_s = ggplot(plot_df_sig, aes(x = lineage
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, fill = sens2)) +
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geom_bar(stat = 'count') +
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facet_wrap_paginate(~mutationinformation
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scale_fill_manual(name = ""
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# name = leg_title
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, values = c("red", "blue")
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#, labels = levels(sens2))
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)+
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facet_wrap(~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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, ncol = 3
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, nrow = 4
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) +
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theme(legend.position = "none" #top
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#, plot.title = element_text(hjust = 0.5, size=15)
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, plot.title = element_blank()
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, strip.text = element_text(size=ts+2)
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, axis.text.x = element_text(size=ts)
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, axis.text.y = element_text(size=ts)
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, axis.title.y = element_text(size=ts)
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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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, y = 'Sample Count') +
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#geom_text(aes(label = pvalRF, x = 2.5, y = ypos_label+0.75))
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geom_blank(aes(y = ypos_label+1.25)) +
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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_label(aes(label = pvalRF, x = 2.5, ypos_label+0.75), fill="white", size =gls)
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#dev.off()
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###################################
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#ns muts
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#svg(paste0(outdir_images, "embb_linDS_ns.svg"), width =10 , height = 8) # old-school square 4:3 CRT shape 1.3:1
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ds_ns = ggplot(plot_df_ns, aes(x = lineage
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, fill = sens2)) +
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geom_bar(stat = 'count') +
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scale_fill_manual(name = ""
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# name = leg_title
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, values = c("red", "blue")
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#, labels = levels(sens2))
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)+
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facet_wrap(~mutationinformation
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, scales = 'free_y'
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#, ncol = 5
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#, nrow = 5
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) +
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theme(legend.position = "none" #top
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#, plot.title = element_text(hjust = 0.5, size=20)
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, plot.title = element_blank()
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, strip.text = element_text(size=ts)
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, axis.text.x = element_text(size=ts)
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, axis.text.y = element_text(size=ts)
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, axis.title.y = element_text(size=ts)
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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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#dev.off()
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# svg(paste0(outdir_images, "embb_linDS_CL.svg")
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# , width = 11
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# , height = 8 )
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png(paste0(outdir_images, "embb_linDS_CL.png")
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, width = 11.75
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, height = 8, units = "in", res = 300 )
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cowplot::plot_grid(ds_s, ds_ns
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, ncol = 2
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,rel_widths = c(1,2)
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, labels = "AUTO")
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dev.off()
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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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@ -21,6 +21,47 @@ geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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# LigDist_colname # from globals used
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# ppi2Dist_colname #from globals used
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# naDist_colname #from globals used
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common_cols = c("mutationinformation"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity", dist_columns )
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########################################
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categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
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fact_cols = colnames(merged_df3)[categ_cols_to_factor]
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if (any(lapply(merged_df3[, fact_cols], class) == "character")){
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cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
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merged_df3[, fact_cols] <- lapply(merged_df3[, fact_cols], as.factor)
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if (all(lapply(merged_df3[, fact_cols], class) == "factor")){
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cat("\nSuccessful: cols changed to factor")
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}
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}else{
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cat("\nRequested cols aready factors")
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}
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cat("\ncols changed to factor are:\n", colnames(merged_df3)[categ_cols_to_factor] )
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####################################
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# merged_df3: NECESSARY pre-processing
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###################################
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#df3 = merged_df3
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plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
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, all_cols)
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all_cols = c(common_cols
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, all_stability_cols
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, all_affinity_cols
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, all_conserv_cols)
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# counting
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foo = merged_df3[, c("mutationinformation"
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