import commit

mcsm_analysis/pyrazinamide/scripts/plotting/barplots_subcolours_PS.R

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+getwd()
+setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
+getwd()
+
+########################################################################
+# 		Installing and loading required packages and functions		   #	
+########################################################################
+
+source("../Header_TT.R")
+source("../barplot_colour_function.R")
+
+########################################################################
+#		 Read file: call script for combining df for PS			   	   #
+########################################################################
+
+source("../combining_two_df.R")
+
+#---------------------- PAY ATTENTION
+# the above changes the working dir
+#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
+#---------------------- PAY ATTENTION
+
+#==========================
+# This will return:
+
+# df with NA:
+# merged_df2
+# merged_df3
+
+# df without NA:
+# merged_df2_comp
+# merged_df3_comp
+#===========================
+
+###########################
+# Data for DUET plots
+# you need merged_df3
+# or
+# merged_df3_comp
+# since these have unique SNPs
+# I prefer to use the merged_df3
+# because using the _comp dataset means
+# we lose some muts and at this level, we should use
+# as much info as available
+###########################
+
+# uncomment as necessary
+#<<<<<<<<<<<<<<<<<<<<<<<<<
+# REASSIGNMENT
+my_df  = merged_df3 
+#my_df  = merged_df3_comp
+#<<<<<<<<<<<<<<<<<<<<<<<<<
+
+# delete variables not required
+rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
+
+# quick checks
+colnames(my_df)
+str(my_df)
+
+# Ensure correct data type in columns to plot: need to be factor
+# sanity check
+is.factor(my_df$DUET_outcome)
+my_df$DUET_outcome = as.factor(my_df$DUET_outcome)
+is.factor(my_df$DUET_outcome)
+#[1] TRUE
+
+########################################################################
+#               end of data extraction and cleaning for plots          #
+########################################################################
+
+#==========================
+# Barplot with scores (unordered)
+# corresponds to DUET_outcome
+# Stacked Barplot with colours: DUET_outcome @ position coloured by 
+# stability scores. This is a barplot where each bar corresponds 
+# to a SNP and is coloured by its corresponding DUET stability value.
+# Normalised values (range between -1 and 1 ) to aid visualisation
+# NOTE: since barplot plots discrete values, colour = score, so number of
+# colours will be equal to the no. of unique normalised scores 
+# rather than a continuous scale
+# will require generating the colour scale separately.
+#============================
+
+#===================
+# Data for plots
+#===================
+
+#<<<<<<<<<<<<<<<<<<<<<<<<
+# REASSIGNMENT
+df  = my_df 
+#<<<<<<<<<<<<<<<<<<<<<<<<<
+
+rm(my_df)
+
+# sanity checks
+upos = unique(df$Position)
+
+# should be a factor
+is.factor(my_df$DUET_outcome)
+#[1] TRUE
+
+table(df$DUET_outcome)
+
+# should be -1 and 1
+min(df$ratioDUET)
+max(df$ratioDUET)
+
+tapply(df$ratioDUET, df$DUET_outcome, min)
+tapply(df$ratioDUET, df$DUET_outcome, max)
+
+#******************
+# generate plot
+#******************
+
+# set output dir for plots
+getwd()
+setwd("~/git/Data/pyrazinamide/output/plots")
+getwd()
+
+# My colour FUNCTION: based on group and subgroup
+# in my case;
+# df = df
+# group = DUET_outcome
+# subgroup = normalised score i.e ratioDUET
+
+# Prepare data: round off ratioDUET scores
+# round off to 3 significant digits:
+# 323 if no rounding is performed: used to generate the original graph
+# 287 if rounded to 3 places
+# FIXME: check if reducing precicion creates any ML prob
+
+# check unique values in normalised data
+u = unique(df$ratioDUET) 
+
+# <<<<< -------------------------------------------
+# Run this section if rounding is to be used
+# specify number for rounding
+n = 3 
+df$ratioDUETR = round(df$ratioDUET, n)
+u = unique(df$ratioDUETR)
+# create an extra column called group which contains the "gp name and score" 
+# so colours can be generated for each unique values in this column
+my_grp = df$ratioDUETR
+df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "")
+
+# else 
+# uncomment the below if rounding is not required
+
+#my_grp = df$ratioDUET
+#df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "")
+
+# <<<<< -----------------------------------------------
+
+# Call the function to create the palette based on the group defined above
+colours <- ColourPalleteMulti(df, "DUET_outcome", "my_grp")
+my_title = "Protein stability (DUET)"
+
+# axis label size
+my_xaxls = 13
+my_yaxls = 15
+
+# axes text size
+my_xaxts = 15
+my_yaxts = 15
+
+# no ordering of x-axis
+g = ggplot(df, aes(factor(Position, ordered = T)))
+g + 
+  geom_bar(aes(fill = group), colour = "grey") +
+  scale_fill_manual( values = colours
+                     , guide = 'none') +
+  theme( axis.text.x = element_text(size = my_xaxls
+                                    , angle = 90
+                                    , hjust = 1
+                                    , vjust = 0.4)
+         , axis.text.y = element_text(size = my_yaxls 
+                                      , angle = 0
+                                      , hjust = 1
+                                      , vjust = 0)
+         , axis.title.x = element_text(size = my_xaxts)
+         , axis.title.y = element_text(size = my_yaxts ) ) +
+  labs(title = my_title
+       , x = "Position"
+       , y = "Frequency")
+
+# for sanity and good practice
+rm(df)
+#======================= end of plot
+# axis colours labels
+# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
+# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label