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generated embb lineage plots

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This commit is contained in:
Tanushree Tunstall 2022-08-13 21:17:39 +01:00
parent c8f3ddf892
commit d984d283c5
3 changed files with 208 additions and 64 deletions
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6
config/embb.R
View file

@ -113,6 +113,6 @@ cat("\n==================================================="
# aa_pos_lig2 = aa_pos_cdl # aa_pos_lig2 = aa_pos_cdl
# aa_pos_lig3 = aa_pos_dsl # aa_pos_lig3 = aa_pos_dsl
aa_pos_lig1 = aa_pos_dsl aa_pos_lig1 = aa_pos_dsl #slategray
aa_pos_lig2 = aa_pos_cdl aa_pos_lig2 = aa_pos_cdl #navy blue
aa_pos_lig3 = aa_pos_ca aa_pos_lig3 = aa_pos_ca #purple

225
scripts/plotting/LINEAGE2.R
View file

@ -11,46 +11,55 @@ library(svglite)
# gene must be lowercase # gene must be lowercase
# tolower(gene) # tolower(gene)
################################################# #################################################
gene="pncA" #gene="pncA"
drug="pyrazinamide" #drug="pyrazinamide"
lineage_filename=paste0(tolower(gene),"_merged_df2.csv") #lineage_filename=paste0(tolower(gene),"_merged_df2.csv")
lineage_data_path="~/git/Data/pyrazinamide/output" #lineage_data_path="~/git/Data/pyrazinamide/output"
df = read.csv(paste0(lineage_data_path,"/",lineage_filename)) df2 = read.csv(paste0(lineage_data_path,"/",lineage_filename))
#df2 = read.csv("/home/tanu/git/Data/pyrazinamide/output/pnca_merged_df3.csv")
foo = as.data.frame(colnames(df)) foo = as.data.frame(colnames(df2))
cols_to_subset = c('mutationinformation' cols_to_subset = c('mutationinformation'
, 'snp_frequency' , 'snp_frequency'
, 'pos_count' , 'pos_count'
, 'position' , 'position'
, 'lineage' , 'lineage'
, 'lineage_multimode' , "sensitivity"
#, 'lineage_multimode'
, 'dst' , 'dst'
, 'dst_multimode' , 'dst2'
#, 'dst_multimode'
#, 'dst_multimode_all' #, 'dst_multimode_all'
, 'dst_mode') , 'dst_mode')
my_df = df[ ,cols_to_subset] #cols_to_subset%in%foo
#df2 = df2[ ,cols_to_subset]
r24p_embb = df_embb[df_embb$mutationinformation == "R24P",] my_df = df2[ ,cols_to_subset]
tm = c("A102P", "M1T") # r24p_embb = df_embb[df_embb$mutationinformation == "R24P",]
test = my_df[my_df$mutationinformation%in%tm,] # #tm = c("A102P", "M1T")
#test$dst2[is.na(test$dst)] <-test$dst_mode # test = my_df[my_df$mutationinformation%in%tm,]
test$dst2 = ifelse(is.na(test$dst), test$dst_mode, test$dst) # #test$dst2[is.na(test$dst)] <-test$dst_mode
sum(table(test$dst2)) == nrow(test) # test$dst2 = ifelse(is.na(test$dst), test$dst_mode, test$dst)
# sum(table(test$dst2)) == nrow(test)
# already exist now
#---------------------------------
# Now we need to make a column that fill na in dst with value of dst_mode # Now we need to make a column that fill na in dst with value of dst_mode
my_df$dst2 = ifelse(is.na(my_df$dst), my_df$dst_mode, my_df$dst) #my_df$dst2_check = ifelse(is.na(my_df$dst), my_df$dst_mode, my_df$dst)
#all(my_df$dst2_check ==my_df$dst2)
#%% create sensitivity column ~ dst_mode #%% create sensitivity column ~ dst2[revised]
my_df$sensitivity = ifelse(my_df$dst2 == 1, "R", "S") my_df$sens2 = ifelse(my_df$dst2 == 1, "R", "S")
#---------------------------------
table(my_df$sens2)
table(my_df$dst2) table(my_df$dst2)
if ( table(my_df$sensitivity)[2] == table(my_df$dst2)[1] && table(my_df$sensitivity)[1] == table(my_df$dst2)[2] ){
if ( table(my_df$sens2 )[2] == table(my_df$dst2)[1] && table(my_df$sens2 )[1] == table(my_df$dst2)[2] ){
cat("\nProceeding with lineage resistance plots") cat("\nProceeding with lineage resistance plots")
}else{ }else{
stop("FAIL: could not verify dst2 and sensitivity numbers") stop("FAIL: could not verify dst2 and sensitivity numbers")
@ -72,30 +81,39 @@ table(my_df2$lineage)
# %% # %%
# str(my_df2) # str(my_df2)
# my_df2$lineage = as.factor(my_df2$lineage) # my_df2$lineage = as.factor(my_df2$lineage)
# my_df2$sensitivity = as.factor(my_df2$sensitivity) # my_df2$sens2 = as.factor(my_df2$sens2)
#%% get only muts which belong to > 1 lineage and have different sensitivity classifications #%% get only muts which belong to > 1 lineage and have different sensitivity classifications
muts = unique(my_df2$mutationinformation) muts = unique(my_df2$mutationinformation)
#----------------------------------------------- #-----------------------------------------------
# step1 : get muts with more than one lineage # step 0 : get muts with more than one lineage
#----------------------------------------------- #-----------------------------------------------
lin_muts = NULL lin_muts = NULL
for (i in muts) { for (i in muts) {
print (i) print (i)
s_mut = my_df2[my_df2$mutationinformation == i,] s_mut = my_df2[my_df2$mutationinformation == i,]
s_tab = table(s_mut$lineage, s_mut$sensitivity) s_tab = table(s_mut$lineage, s_mut$sens2)
#print(s_tab) #print(s_tab)
if (dim(s_tab)[1] > 1 && dim(s_tab)[2] > 1){ if (dim(s_tab)[1] > 1 && dim(s_tab)[2] > 1){
lin_muts = c(lin_muts, i) lin_muts = c(lin_muts, i)
} }
} }
cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities") cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities")
#-----------------------------------------------
# step 1 : get other muts that do not have this
#-----------------------------------------------
consist_muts = muts[!muts%in%lin_muts]
cat("\nGot:", length(consist_muts), "mutations that are consistent")
#----------------------------------------------- #-----------------------------------------------
# step 2: subset these muts for plotting # step 2: subset these muts for plotting
#----------------------------------------------- #-----------------------------------------------
plot_df = my_df2[my_df2$mutationinformation%in%lin_muts,] plot_df = my_df2[my_df2$mutationinformation%in%lin_muts,]
cat("\nnrow of plot_df:", nrow(plot_df)) cat("\nnrow of plot_df:", nrow(plot_df))
#----------------------------------------------- #-----------------------------------------------
# step 3: Add p-value # step 3: Add p-value
#----------------------------------------------- #-----------------------------------------------
@ -104,7 +122,7 @@ for (i in lin_muts) {
#print (i) #print (i)
s_mut = plot_df[plot_df$mutationinformation == i,] s_mut = plot_df[plot_df$mutationinformation == i,]
#print(s_mut) #print(s_mut)
s_tab = table(s_mut$lineage, s_mut$sensitivity) s_tab = table(s_mut$lineage, s_mut$sens2)
#print(s_tab) #print(s_tab)
#ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3) #ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3)
ft_pvalue_i = fisher.test(s_tab)$p.value ft_pvalue_i = fisher.test(s_tab)$p.value
@ -114,11 +132,30 @@ for (i in lin_muts) {
} }
plot_df$pvalR = round(plot_df$pval, 3) plot_df$pvalR = round(plot_df$pval, 3)
plot_df$pvalRF = ifelse(plot_df$pvalR == 0.05, paste0("p=",plot_df$pvalR, "."), plot_df$pvalR ) # plot_df$pvalRF = ifelse(plot_df$pvalR == 0.05, paste0("p=",plot_df$pvalR, "."), plot_df$pvalR )
plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.05, paste0("p=",plot_df$pvalR, "*"), plot_df$pvalRF ) # plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.05, paste0("p=",plot_df$pvalR, "*"), plot_df$pvalRF )
plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.01, paste0("p=",plot_df$pvalR, "**"), plot_df$pvalRF ) # plot_df$pvalRF = ifelse(plot_df$pvalR <= 0.01, paste0("p=",plot_df$pvalR, "**"), plot_df$pvalRF )
plot_df$pvalRF = ifelse(plot_df$pvalR == 0, 'p<0.001, ***', plot_df$pvalRF) # plot_df$pvalRF = ifelse(plot_df$pvalR == 0, 'p<0.001, ***', plot_df$pvalRF)
plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_df$pvalRF) # plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_df$pvalRF)
# plot_df
# plot_df$pvalRF_old = plot_df$pvalRF
plot_df$pvalRF = plot_df$pvalR
plot_df = dplyr::mutate(plot_df
, pvalRF = case_when(pvalRF == 0.05 ~ "P ."
, pvalRF <=0.0001 ~ 'P ****'
, pvalRF <=0.001 ~ 'P ***'
, pvalRF <=0.01 ~ 'P **'
, pvalRF <0.05 ~ 'P *'
, TRUE ~ 'ns'))
plot_df
head(plot_df)
table(plot_df$pvalR<0.05)
# format p value # format p value
# TODO: add case statement for correct pvalue formatting # TODO: add case statement for correct pvalue formatting
@ -149,8 +186,8 @@ plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_d
# #
# # Calculate stats: example # # Calculate stats: example
# test2 = plot_df[plot_df$mutationinformation%in%tm2,] # test2 = plot_df[plot_df$mutationinformation%in%tm2,]
# table(test2$mutationinformation, test2$lineage, test2$sensitivity) # table(test2$mutationinformation, test2$lineage, test2$sens2)
# tm_tab = table(test2$lineage, test2$sensitivity) # tm_tab = table(test2$lineage, test2$sens2)
# tm_tab # tm_tab
# Get the ypos for plotting the label for p-value # Get the ypos for plotting the label for p-value
@ -179,13 +216,26 @@ if (nrow(lin_muts_dfM) == nrow(plot_df) ){
cat("\nPASS: plot_df now has ypos for label" cat("\nPASS: plot_df now has ypos for label"
, "\nGenerating plot_df2 with sensitivity as factor\n") , "\nGenerating plot_df2 with sensitivity as factor\n")
str(lin_muts_dfM) str(lin_muts_dfM)
lin_muts_dfM$sensitivity = as.factor(lin_muts_dfM$sensitivity) lin_muts_dfM$sens2 = as.factor(lin_muts_dfM$sens2)
plot_df2 = lin_muts_dfM plot_df2 = lin_muts_dfM
}else{ }else{
stop("\nSomething went wrong. ypos_label could not be generated") stop("\nSomething went wrong. ypos_label could not be generated")
} }
# sig muts
plot_df_sig = plot_df2[plot_df2$pvalR<0.05,]
sig_muts = length(unique(plot_df_sig$mutationinformation))
cat("\nGot:", sig_muts, "mutations that are significant")
plot_df_ns = plot_df2[plot_df2$pvalR>0.05,]
ns_muts = length(unique(plot_df_ns$mutationinformation))
cat("\nGot:", ns_muts, "mutations that are NOT significant")
p_title = gene
ts = 8
gls = 3
#================================================ #================================================
# Plot: with stats (plot_df2) # Plot: with stats (plot_df2)
# TODO: # TODO:
@ -194,41 +244,94 @@ if (nrow(lin_muts_dfM) == nrow(plot_df) ){
#3) Add *: Extend yaxis for each plot to allow geom_label to have space (or see #3) Add *: Extend yaxis for each plot to allow geom_label to have space (or see
# if this self resolving with facet_wrap_paginate()) # if this self resolving with facet_wrap_paginate())
#================================================ #================================================
plot_pages = round(length(lin_muts)/25) #svg(paste0(outdir_images, "embb_linDS.svg"), width = 6, height = 10 ) # old-school square 4:3 CRT shape 1.3:1
p_title = gene ds_s = ggplot(plot_df_sig, aes(x = lineage
res = 144 # SVG dots-per-inch , fill = sens2)) +
sapply(1:plot_pages, function(page){
print(paste0("Plotting page:", page))
svglite(paste0("/tmp/output-",page,".svg"), width=2048/res, height=1534/res) # old-school square 4:3 CRT shape 1.3:1
print(
ggplot(plot_df2, aes(x = lineage
, fill = sensitivity)) +
geom_bar(stat = 'count') + geom_bar(stat = 'count') +
facet_wrap_paginate(~mutationinformation scale_fill_manual(name = ""
# name = leg_title
, values = c("red", "blue")
#, labels = levels(sens2))
)+
facet_wrap(~mutationinformation
, scales = 'free_y' , scales = 'free_y'
, ncol = 5 , ncol = 3
, nrow = 5 , nrow = 4
, page = page) + ) +
theme(legend.position = "top" theme(legend.position = "none" #top
, plot.title = element_text(hjust = 0.5, size=20) #, plot.title = element_text(hjust = 0.5, size=15)
, strip.text = element_text(size=14) , plot.title = element_blank()
, axis.text.x = element_text(size=14)
, axis.text.y = element_text(size=14) , strip.text = element_text(size=ts+2)
, axis.title.y = element_text(size=14) , axis.text.x = element_text(size=ts)
, axis.text.y = element_text(size=ts)
, axis.title.y = element_text(size=ts)
, legend.title = element_blank() , legend.title = element_blank()
, axis.title.x = element_blank() , axis.title.x = element_blank()
)+ )+
labs(title = paste0(p_title, ": sensitivity by lineage") labs(title = paste0(p_title, ": sensitivity by lineage")
, y = 'Sample Count' , y = 'Sample Count') +
) + #geom_text(aes(label = pvalRF, x = 2.5, y = ypos_label+0.75))
#geom_text(aes(label = p.value, x = 0.5, y = 5))
geom_blank(aes(y = ypos_label+1.25)) + geom_blank(aes(y = ypos_label+1.25)) +
geom_label(aes(label = pvalRF, x = 2.5, ypos_label+0.75), fill="white") geom_label(aes(label = pvalRF, x = 2.5, ypos_label+0.75), fill="white", size =gls)
)
dev.off() #dev.off()
}
) ###################################
#ns muts
#svg(paste0(outdir_images, "embb_linDS_ns.svg"), width =10 , height = 8) # old-school square 4:3 CRT shape 1.3:1
ds_ns = ggplot(plot_df_ns, aes(x = lineage
, fill = sens2)) +
geom_bar(stat = 'count') +
scale_fill_manual(name = ""
# name = leg_title
, values = c("red", "blue")
#, labels = levels(sens2))
)+
facet_wrap(~mutationinformation
, scales = 'free_y'
#, ncol = 5
#, nrow = 5
) +
theme(legend.position = "none" #top
#, plot.title = element_text(hjust = 0.5, size=20)
, plot.title = element_blank()
, strip.text = element_text(size=ts)
, axis.text.x = element_text(size=ts)
, axis.text.y = element_text(size=ts)
, axis.title.y = element_text(size=ts)
, legend.title = element_blank()
, axis.title.x = element_blank()
)+
labs(title = paste0(p_title, ": sensitivity by lineage")
, y = 'Sample Count')
#dev.off()
# svg(paste0(outdir_images, "embb_linDS_CL.svg")
# , width = 11
# , height = 8 )
png(paste0(outdir_images, "embb_linDS_CL.png")
, width = 11.75
, height = 8, units = "in", res = 300 )
cowplot::plot_grid(ds_s, ds_ns
, ncol = 2
,rel_widths = c(1,2)
, labels = "AUTO")
dev.off()
#geom_text(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1))# + #geom_text(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1))# +
#geom_segment(aes(x = 1, y = ypos_label+0.5, xend = 4, yend = ypos_label+0.5)) #geom_segment(aes(x = 1, y = ypos_label+0.5, xend = 4, yend = ypos_label+0.5))

41
scripts/plotting/plotting_thesis/preformatting.R
View file

@ -21,6 +21,47 @@ geneL_normal = c("pnca")
geneL_na = c("gid", "rpob") geneL_na = c("gid", "rpob")
geneL_ppi2 = c("alr", "embb", "katg", "rpob") geneL_ppi2 = c("alr", "embb", "katg", "rpob")
# LigDist_colname # from globals used
# ppi2Dist_colname #from globals used
# naDist_colname #from globals used
common_cols = c("mutationinformation"
, "X5uhc_position"
, "X5uhc_offset"
, "position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity", dist_columns )
########################################
categ_cols_to_factor = grep( "_outcome|_info", colnames(merged_df3) )
fact_cols = colnames(merged_df3)[categ_cols_to_factor]
if (any(lapply(merged_df3[, fact_cols], class) == "character")){
cat("\nChanging", length(categ_cols_to_factor), "cols to factor")
merged_df3[, fact_cols] <- lapply(merged_df3[, fact_cols], as.factor)
if (all(lapply(merged_df3[, fact_cols], class) == "factor")){
cat("\nSuccessful: cols changed to factor")
}
}else{
cat("\nRequested cols aready factors")
}
cat("\ncols changed to factor are:\n", colnames(merged_df3)[categ_cols_to_factor] )
####################################
# merged_df3: NECESSARY pre-processing
###################################
#df3 = merged_df3
plot_cols = c("mutationinformation", "mutation_info_labels", "position", "dst_mode"
, all_cols)
all_cols = c(common_cols
, all_stability_cols
, all_affinity_cols
, all_conserv_cols)
# counting # counting
foo = merged_df3[, c("mutationinformation" foo = merged_df3[, c("mutationinformation"