tanu/LSHTM_analysis
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

generated lineage dist plots combined. needs tweaking

Browse source
This commit is contained in:
Tanushree Tunstall 2020-09-09 12:53:53 +01:00
parent 31b98fb3d3
commit 19a984f228
3 changed files with 191 additions and 119 deletions
Download patch file Download diff file Expand all files Collapse all files

2
scripts/plotting/combining_dfs_plotting.R
View file

@ -48,6 +48,8 @@ cat("Variables imported:"
, "\ndrug:", drug , "\ndrug:", drug
, "\ngene:", gene , "\ngene:", gene
, "\ngene_match:", gene_match , "\ngene_match:", gene_match
, "\nAngstrom symbol:", angstroms_symbol
, "\nNo. of duplicated muts:", dup_muts_nu
, "\ndr_muts_col:", dr_muts_col , "\ndr_muts_col:", dr_muts_col
, "\nother_muts_col:", other_muts_col , "\nother_muts_col:", other_muts_col
, "\ndrtype_col:", resistance_col) , "\ndrtype_col:", resistance_col)

15
scripts/plotting/lineage_basic_barplot.R
View file

@ -30,13 +30,15 @@ source("combining_dfs_plotting.R")
# 9) my_df_u # 9) my_df_u
# 10) my_df_u_lig # 10) my_df_u_lig
cat(paste0("Directories imported:" cat("Directories imported:"
, "\n===================="
, "\ndatadir:", datadir , "\ndatadir:", datadir
, "\nindir:", indir , "\nindir:", indir
, "\noutdir:", outdir , "\noutdir:", outdir
, "\nplotdir:", plotdir)) , "\nplotdir:", plotdir)
cat(paste0("Variables imported:" cat("Variables imported:"
, "\n====================="
, "\ndrug:", drug , "\ndrug:", drug
, "\ngene:", gene , "\ngene:", gene
, "\ngene_match:", gene_match , "\ngene_match:", gene_match
@ -44,7 +46,11 @@ cat(paste0("Variables imported:"
, "\nNo. of duplicated muts:", dup_muts_nu , "\nNo. of duplicated muts:", dup_muts_nu
, "\nNA count for ORs:", na_count , "\nNA count for ORs:", na_count
, "\nNA count in df2:", na_count_df2 , "\nNA count in df2:", na_count_df2
, "\nNA count in df3:", na_count_df3)) , "\nNA count in df3:", na_count_df3
, "\ndr_muts_col:", dr_muts_col
, "\nother_muts_col:", other_muts_col
, "\ndrtype_col:", resistance_col)
#=========== #===========
# input # input
@ -67,7 +73,6 @@ plot_basic_bp_lineage = paste0(plotdir,"/", basic_bp_lineage)
#================ #================
# REASSIGNMENT as necessary # REASSIGNMENT as necessary
my_df = merged_df2 my_df = merged_df2
#my_df = merged_df2_comp
# clear excess variable # clear excess variable
rm(merged_df2_comp, merged_df3, merged_df3_comp) rm(merged_df2_comp, merged_df3, merged_df3_comp)

273
scripts/plotting/lineage_dist_PS.R
View file

@ -3,36 +3,55 @@ getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting/") setwd("~/git/LSHTM_analysis/scripts/plotting/")
getwd() getwd()
######################################################################## #########################################################
# Installing and loading required packages # # TASK: Lineage dist plots: ggridges
########################################################################
source("../Header_TT.R") # Output: 2 SVGs for PS stability
#source("../barplot_colour_function.R")
#require(data.table)
######################################################################## # 1) all muts
# Read file: call script for combining df for PS # # 2) dr_muts
########################################################################
source("combining_two_df.R") ##########################################################
# Installing and loading required packages
##########################################################
#---------------------- PAY ATTENTION source("combining_dfs_plotting.R")
# the above changes the working dir # PS combined:
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts" # 1) merged_df2
#---------------------- PAY ATTENTION # 2) merged_df2_comp
# 3) merged_df3
# 4) merged_df3_comp
#========================== # LIG combined:
# This will return: # 5) merged_df2_lig
# 6) merged_df2_comp_lig
# 7) merged_df3_lig
# 8) merged_df3_comp_lig
# df with NA for <drug> # 9) my_df_u
# merged_df2 # 10) my_df_u_lig
# merged_df3
cat("Directories imported:"
, "\n===================="
, "\ndatadir:", datadir
, "\nindir:", indir
, "\noutdir:", outdir
, "\nplotdir:", plotdir)
cat("Variables imported:"
, "\n====================="
, "\ndrug:", drug
, "\ngene:", gene
, "\ngene_match:", gene_match
, "\nAngstrom symbol:", angstroms_symbol
, "\nNo. of duplicated muts:", dup_muts_nu
, "\nNA count for ORs:", na_count
, "\nNA count in df2:", na_count_df2
, "\nNA count in df3:", na_count_df3
, "\ndr_muts_col:", dr_muts_col
, "\nother_muts_col:", other_muts_col
, "\ndrtype_col:", resistance_col)
# df without NA for <drug>
# merged_df2_comp
# merged_df3_comp
#===========================
########################### ###########################
# Data for plots # Data for plots
@ -43,13 +62,8 @@ source("combining_two_df.R")
# we lose some muts and at this level, we should use # we lose some muts and at this level, we should use
# as much info as available, hence use df with NA # as much info as available, hence use df with NA
########################### ###########################
# uncomment as necessary
#%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT # REASSIGNMENT
my_df = merged_df2 my_df = merged_df2
#my_df = merged_df2_comp
#%%%%%%%%%%%%%%%%%%%%%%%%
# delete variables not required # delete variables not required
rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp) rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
@ -63,7 +77,7 @@ is.factor(my_df$lineage)
my_df$lineage = as.factor(my_df$lineage) my_df$lineage = as.factor(my_df$lineage)
is.factor(my_df$lineage) is.factor(my_df$lineage)
table(my_df$mutation_info); str(my_df$mutation_info) table(my_df$mutation_info)
# subset df with dr muts only # subset df with dr muts only
my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide") my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide")
@ -74,59 +88,32 @@ table(my_df_dr$mutation_info)
######################################################################## ########################################################################
#========================== #==========================
# Run two times: # Plot 1: ALL Muts
# uncomment as necessary
# 1) for all muts
# 2) for dr_muts
#===========================
#%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
#================
# for ALL muts
#================
plot_df = my_df
my_plot_name = 'lineage_dist_PS.svg'
plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
#my_plot_name = 'lineage_dist_PS_comp.svg'
#================
# for dr muts ONLY
#================
plot_df = my_df_dr
#my_plot_name = 'lineage_dist_dr_PS.svg'
#my_plot_name = 'lineage_dist_dr_PS_comp.svg'
my_plot_name = 'lineage_dist_drug_muts_PS.svg'
plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
#%%%%%%%%%%%%%%%%%%%%%%%%
#==========================
# Plot: Lineage Distribution
# x = mcsm_values, y = dist # x = mcsm_values, y = dist
# fill = stability # fill = stability
#============================ #============================
my_plot_name = 'lineage_dist_PS.svg'
plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
#=================== #===================
# Data for plots # Data for plots
#=================== #===================
table(plot_df$lineage); str(plot_df$lineage) table(my_df$lineage); str(my_df$lineage)
# subset only lineages1-4 # subset only lineages1-4
sel_lineages = c("lineage1" sel_lineages = c("lineage1"
, "lineage2" , "lineage2"
, "lineage3" , "lineage3"
, "lineage4") , "lineage4"
#, "lineage5" #, "lineage5"
#, "lineage6" #, "lineage6"
#, "lineage7") #, "lineage7"
)
# uncomment as necessary # uncomment as necessary
df_lin = subset(plot_df, subset = lineage %in% sel_lineages ) df_lin = subset(my_df, subset = lineage %in% sel_lineages )
table(df_lin$lineage) table(df_lin$lineage)
# refactor # refactor
@ -139,16 +126,8 @@ table(df_lin$lineage)#{RESULT: No of samples within lineage}
length(unique(df_lin$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to} length(unique(df_lin$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
length(df_lin$mutationinformation) length(df_lin$mutationinformation)
# sanity checks
# FIXME
r1 = 2:7 # when merged_df2 used: because there is missing lineages
if(sum(table(plot_df$lineage)[r1]) == nrow(df_lin)) {
print ("sanity check passed: numbers match")
} else{
print("Error!: check your numbers")
}
u2 = unique(plot_df$mutationinformation) u2 = unique(my_df$mutationinformation)
u = unique(df_lin$mutationinformation) u = unique(df_lin$mutationinformation)
check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages} check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
@ -173,11 +152,11 @@ names(my_labels) = c('lineage1', 'lineage2', 'lineage3', 'lineage4'
# , 'lineage5', 'lineage6', 'lineage7' # , 'lineage5', 'lineage6', 'lineage7'
) )
# check plot name # check plot name
my_plot_name plot_lineage_duet
# output svg # output svg
svg(plot_lineage_duet) #svg(plot_lineage_duet)
printFile = ggplot(df, aes(x = duet_scaled p1 = ggplot(df, aes(x = duet_scaled
, y = duet_outcome))+ , y = duet_outcome))+
#printFile=geom_density_ridges_gradient( #printFile=geom_density_ridges_gradient(
@ -186,47 +165,133 @@ printFile = ggplot(df, aes(x = duet_scaled
, size = 0.3 ) + , size = 0.3 ) +
facet_wrap( ~lineage facet_wrap( ~lineage
, scales = "free" , scales = "free"
# , switch = 'x' #, switch = 'x'
, labeller = labeller(lineage = my_labels) ) + , labeller = labeller(lineage = my_labels) ) +
coord_cartesian( xlim = c(-1, 1) coord_cartesian( xlim = c(-1, 1)) +
# , ylim = c(0, 6)
# , clip = "off"
) +
scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4") scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
, name = "DUET" ) + , name = "DUET" ) +
theme(axis.text.x = element_text(size = my_ats theme(axis.text.x = element_text(size = my_ats
, angle = 90 , angle = 90
, hjust = 1 , hjust = 1
, vjust = 0.4) , vjust = 0.4)
# , axis.text.y = element_text(size = my_ats
# , angle = 0
# , hjust = 1
# , vjust = 0)
, axis.text.y = element_blank() , axis.text.y = element_blank()
, axis.title.x = element_blank() , axis.title.x = element_blank()
, axis.title.y = element_blank() , axis.title.y = element_blank()
, axis.ticks.y = element_blank() , axis.ticks.y = element_blank()
, plot.title = element_blank() , plot.title = element_blank()
, strip.text = element_text(size = my_als) , strip.text = element_text(size = my_als)
, legend.text = element_text(size = 10) , legend.text = element_text(size = my_als-5)
, legend.title = element_text(size = my_als) , legend.title = element_text(size = my_als)
# , legend.position = c(0.3, 0.8) )
# , legend.key.height = unit(1, 'mm')
print(p1)
#dev.off()
#######################################################################
# lineage distribution plot for dr_muts
#######################################################################
#==========================
# Plot 2: dr muts ONLY
# x = mcsm_values, y = dist
# fill = stability
#============================
my_plot_name_dr = 'lineage_dist_dr_muts_PS.svg'
plot_lineage_dr_duet = paste0(plotdir,"/", my_plot_name_dr)
#===================
# Data for plots
#===================
table(my_df_dr$lineage); str(my_df_dr$lineage)
# uncomment as necessary
df_lin_dr = subset(my_df_dr, subset = lineage %in% sel_lineages)
table(df_lin_dr$lineage)
# refactor
df_lin_dr$lineage = factor(df_lin_dr$lineage)
sum(table(df_lin_dr$lineage)) #{RESULT: Total number of samples for lineage}
table(df_lin_dr$lineage)#{RESULT: No of samples within lineage}
length(unique(df_lin_dr$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
length(df_lin_dr$mutationinformation)
u2 = unique(my_df_dr$mutationinformation)
u = unique(df_lin_dr$mutationinformation)
check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
#%%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
df_dr <- df_lin_dr
#%%%%%%%%%%%%%%%%%%%%%%%%%
rm(df_lin_dr)
#******************
# generate distribution plot of lineages
#******************
# 2 : ggridges (good!)
my_ats = 15 # axis text size
my_als = 20 # axis label size
# check plot name
plot_lineage_dr_duet
# output svg
#svg(plot_lineage_dr_duet)
p2 = ggplot(df_dr, aes(x = duet_scaled
, y = duet_outcome))+
#printFile=geom_density_ridges_gradient(
geom_density_ridges_gradient(aes(fill = ..x..)
, scale = 3
, size = 0.3 ) +
facet_wrap( ~lineage
, scales = "free"
#, switch = 'x'
, labeller = labeller(lineage = my_labels) ) +
coord_cartesian( xlim = c(-1, 1)
#, ylim = c(0, 6)
#, clip = "off"
) +
scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
, name = "DUET" ) +
theme(axis.text.x = element_text(size = my_ats
, angle = 90
, hjust = 1
, vjust = 0.4)
, axis.text.y = element_blank()
, axis.title.x = element_blank()
, axis.title.y = element_blank()
, axis.ticks.y = element_blank()
, plot.title = element_blank()
, strip.text = element_text(size = my_als)
#, legend.text = element_text(size = 10)
#, legend.title = element_text(size = my_als)
, legend.position = "none"
) )
print(p2)
#dev.off()
#====================
#combine
#=======
# output
#=======
lineage_dist_combined = "lineage_dist_combined_PS.svg"
plot_lineage_dist_combined = paste0(plotdir,"/", lineage_dist_combined)
svg(plot_lineage_dist_combined, width = 16, height = 12)
printFile = cowplot::plot_grid(p1, p2
, label_size = my_als+10)
print(printFile) print(printFile)
dev.off() dev.off()
#=!=!=!=!=!=!=!
# COMMENT: Not much differences in the distributions
# when using merged_df2 or merged_df2_comp.
# Also, the lineage differences disappear when looking at all muts
# The pattern we are interested in is possibly only for dr_mutations
#=!=!=!=!=!=!=!
#===================================================
# COMPARING DISTRIBUTIONS: KS test
# run: "../KS_test_PS.R"