max(my_df2$Dis_lig_Ang) #9.847
min(my_df2$Dis_lig_Ang) #3.047
# count no of unique positions
length(unique(my_df2$Position))#47
#count no of unique mutations
length(unique(my_df2$Mutationinformation)) #110
#clear variable to avoid confusion
rm(my_df)
#%%%%%%%%%%
# Reassign
#%%%%%%%%%%
my_df = my_df2
# Stacked Barplot with colours: Lig_outcome @ position coloured by
# stability scores. This is a barplot where each bar corresponds
# to a SNP and is coloured by its corresponding PredAff 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.
#============================
# sanity checks
upos = unique(my_df$Position)
str(my_df$Lig_outcome)
colnames(my_df)
#===========================
# Data preparation for plots
#===========================
#!!!!!!!!!!!!!!!!!
# REASSIGNMENT
df <- my_df
rm(my_df)
# sanity checks
# should be a factor
is.factor(df$Lig_outcome)
table(df$Lig_outcome)
# sanity checks
# should be a factor
is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
# sanity checks
# should be a factor
is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
#===========================
# Data preparation for plots
#===========================
#!!!!!!!!!!!!!!!!!
# REASSIGNMENT
df <- my_df
getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
getwd()
############################################################
# 1: Installing and loading required packages and functions
############################################################
source("../Header_TT.R")
source("../barplot_colour_function.R")
############################################################
# Output dir for plots
############################################################
out_dir = "~/git/Data/pyrazinamide/output/plots"
############################################################
# 2: call script the prepares the data with columns containing
# colours for axis labels
############################################################
source("subcols_axis.R")
# this should return
#mut_pos_cols: 130, 4
#my_df: 335, 39
# clear excess variable
# "mut_pos_cols" is just for inspection in case you need to cross check
# position numbers and colours
# open file from deskptop ("sample_axis_cols") for cross checking
table(mut_pos_cols$lab_bg)
sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should  be True
table(mut_pos_cols$lab_bg2)
sum( table(mut_pos_cols$lab_bg2) ) ==  nrow(mut_pos_cols) # should  be True
table(mut_pos_cols$lab_fg)
sum( table(mut_pos_cols$lab_fg) ) ==  nrow(mut_pos_cols) # should  be True
# very important!
my_axis_colours = mut_pos_cols$lab_fg
# now clear mut_pos_cols
rm(mut_pos_cols)
########################### !!! only for mcsm_lig
# 4: Filter/subset data
# Lig plots < 10Ang
# Filter the lig plots for Dis_to_lig < 10Ang
###########################
# check range of distances
max(my_df$Dis_lig_Ang)
min(my_df$Dis_lig_Ang)
# subset data to have only values less than 10 Ang
my_df2 = subset(my_df, my_df$Dis_lig_Ang < 10)
# sanity checks
table(my_df2$Dis_lig_Ang<10)
table(my_df2$Dis_lig_Ang>10)
max(my_df2$Dis_lig_Ang)
min(my_df2$Dis_lig_Ang)
# count no of unique positions
length(unique(my_df2$Position))
#count no of unique mutations
length(unique(my_df2$Mutationinformation))
# clear variable to avoid confusion
rm(my_df)
#%%%%%%%%%%
# Reassign to keep code below consistent
#%%%%%%%%%%
my_df = my_df2
###########################
# 2: Plot: Lig scores
###########################
#==========================
# Plot 2: Barplot with scores (unordered)
# corresponds to Lig_outcome
# Stacked Barplot with colours: Lig_outcome @ position coloured by
# stability scores. This is a barplot where each bar corresponds
# to a SNP and is coloured by its corresponding PredAff 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.
#============================
# sanity checks
upos = unique(my_df$Position)
str(my_df$Lig_outcome)
colnames(my_df)
#===========================
# Data preparation for plots
#===========================
#!!!!!!!!!!!!!!!!!
# REASSIGNMENT
df <- my_df
rm(my_df)
# sanity checks
# should be a factor
is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
df$Lig_outcome = as.factor(df$Lig_outcome)
is.factor(df$Lig_outcome);
table(df$Lig_outcome)
# should be -1 and 1
min(df$ratioPredAff)
max(df$ratioPredAff)
# sanity checks
# very important!!!!
tapply(df$ratioPredAff, df$Lig_outcome, min)
tapply(df$ratioPredAff, df$Lig_outcome, max)
# sanity checks
# very important!!!!
tapply(df$ratioPredAff, df$Lig_outcome, min)
tapply(df$ratioPredAff, df$Lig_outcome, max)
# check unique values in normalised data
u = unique(df$ratioPredAff)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Run this section if rounding is to be used
# specify number for rounding
n = 3
df$ratioPredAffR = round(df$ratioPredAff, n)
u = unique(df$ratioPredAffR)
# 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$ratioPredAffR
df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
# Call the function to create the palette based on the group defined above
colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
my_title = "Protein stability (PredAff)"
library(ggplot2)
# axis label size
my_xaxls = 13
my_title = "Ligand Affinity"
# axis label size
my_xaxls = 13
my_yaxls = 15
# axes text size
my_xaxts = 15
my_yaxts = 15
# no ordering of x-axis according to frequency
g = ggplot(df, aes(factor(Position, ordered = T)))
g +
geom_bar(aes(fill = group), colour = "grey") +
scale_fill_manual( values = colours
, guide = 'none') +
theme( axis.text.x = element_text(size = my_xaxls
, angle = 90
, hjust = 1
, vjust = 0.4)
, axis.text.y = element_text(size = my_yaxls
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.x = element_text(size = my_xaxts)
, axis.title.y = element_text(size = my_yaxts ) ) +
labs(title = my_title
, x = "Position"
, y = "Frequency")
class(df$lab_bg)
# make this a named vector
# define cartesian coord
my_xlim = length(unique(df$Position)); my_xlim
# axis label size
my_xals = 15
my_yals = 15
# axes text size
my_xats = 15
my_yats = 18
# using geom_tile
g = ggplot(df, aes(factor(Position, ordered = T)))
g +
coord_cartesian(xlim = c(1, my_xlim)
, ylim = c(0, 6)
, clip = "off") +
geom_bar(aes(fill = group), colour = "grey") +
scale_fill_manual( values = colours
, guide = 'none') +
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
, fill = df$lab_bg) +
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
, fill = df$lab_bg2) +
# Here it's important to specify that your axis goes from 1 to max number of levels
theme( axis.text.x = element_text(size = my_xats
, angle = 90
, hjust = 1
, vjust = 0.4
, colour = my_axis_colours)
, axis.text.y = element_text(size = my_yats
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.x = element_text(size = my_xals)
, axis.title.y = element_text(size = my_yals )
, axis.ticks.x = element_blank()
) +
labs(title = my_title
, x = "Position"
, y = "Frequency")
#========================
# output plot as svg/png
#========================
class(df$lab_bg)
# make this a named vector
# define cartesian coord
my_xlim = length(unique(df$Position)); my_xlim
# axis label size
my_xals = 18
my_yals = 18
# axes text size
my_xats = 14
my_yats = 18
# set output dir for plots
#getwd()
#setwd("~/git/Data/pyrazinamide/output/plots")
#getwd()
getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
getwd()
############################################################
# 1: Installing and loading required packages and functions
############################################################
source("../Header_TT.R")
source("../barplot_colour_function.R")
############################################################
# Output dir for plots
############################################################
out_dir = "~/git/Data/pyrazinamide/output/plots"
############################################################
# 2: call script the prepares the data with columns containing
# colours for axis labels
############################################################
source("subcols_axis.R")
# this should return
#mut_pos_cols: 130, 4
#my_df: 335, 39
# clear excess variable
# "mut_pos_cols" is just for inspection in case you need to cross check
# position numbers and colours
# open file from deskptop ("sample_axis_cols") for cross checking
table(mut_pos_cols$lab_bg)
sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should  be True
table(mut_pos_cols$lab_bg2)
sum( table(mut_pos_cols$lab_bg2) ) ==  nrow(mut_pos_cols) # should  be True
table(mut_pos_cols$lab_fg)
sum( table(mut_pos_cols$lab_fg) ) ==  nrow(mut_pos_cols) # should  be True
# very important!
my_axis_colours = mut_pos_cols$lab_fg
# now clear mut_pos_cols
rm(mut_pos_cols)
########################### !!! only for mcsm_lig
# 4: Filter/subset data
# Lig plots < 10Ang
# Filter the lig plots for Dis_to_lig < 10Ang
###########################
# check range of distances
max(my_df$Dis_lig_Ang)
min(my_df$Dis_lig_Ang)
# subset data to have only values less than 10 Ang
my_df2 = subset(my_df, my_df$Dis_lig_Ang < 10)
# sanity checks
table(my_df2$Dis_lig_Ang<10)
table(my_df2$Dis_lig_Ang>10)
max(my_df2$Dis_lig_Ang)
min(my_df2$Dis_lig_Ang)
# count no of unique positions
length(unique(my_df2$Position))
#count no of unique mutations
length(unique(my_df2$Mutationinformation))
# clear variable to avoid confusion
rm(my_df)
#%%%%%%%%%%
# Reassign to keep code below consistent
#%%%%%%%%%%
my_df = my_df2
###########################
# 2: Plot: Lig scores
###########################
#==========================
# Plot 2: Barplot with scores (unordered)
# corresponds to Lig_outcome
# Stacked Barplot with colours: Lig_outcome @ position coloured by
# stability scores. This is a barplot where each bar corresponds
# to a SNP and is coloured by its corresponding PredAff stability value.
# Normalised values (range between -1 and 1 ) to aid visualisation
# NOTE: since barplot plots discrete values, colour = score, so number of
# colours will be equal to the no. of unique normalised scores
# rather than a continuous scale
# will require generating the colour scale separately.
#============================
# sanity checks
upos = unique(my_df$Position)
str(my_df$Lig_outcome)
colnames(my_df)
#===========================
# Data preparation for plots
#===========================
#!!!!!!!!!!!!!!!!!
# REASSIGNMENT
df <- my_df
#!!!!!!!!!!!!!!!!!
rm(my_df)
# sanity checks
# should be a factor
is.factor(df$Lig_outcome);
#FALSE
df$Lig_outcome = as.factor(df$Lig_outcome)
is.factor(df$Lig_outcome);
#TRUE
table(df$Lig_outcome)
# check the range
min(df$ratioPredAff)
max(df$ratioPredAff)
# sanity checks
# very important!!!!
tapply(df$ratioPredAff, df$Lig_outcome, min)
tapply(df$ratioPredAff, df$Lig_outcome, max)
# My colour FUNCTION: based on group and subgroup
# in my case;
# df = df
# group = Lig_outcome
# subgroup = normalised score i.e ratioPredAff
# Prepare data: round off ratioPredAff 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$ratioPredAff)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Run this section if rounding is to be used
# specify number for rounding
n = 3
df$ratioPredAffR = round(df$ratioPredAff, n)
u = unique(df$ratioPredAffR)
# 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$ratioPredAffR
df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
# ELSE
# uncomment the below if rounding is not required
#my_grp = df$ratioPredAff
#df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#******************
# generate plot
#******************
# Call the function to create the palette based on the group defined above
colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
my_title = "Ligand Affinity"
library(ggplot2)
# axis label size
my_xaxls = 13
my_yaxls = 15
# axes text size
my_xaxts = 15
my_yaxts = 15
# no ordering of x-axis according to frequency
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")
#========================
# plot with axis colours
#========================
class(df$lab_bg)
# make this a named vector
# define cartesian coord
my_xlim = length(unique(df$Position)); my_xlim
# axis label size
my_xals = 15
my_yals = 15
# axes text size
my_xats = 15
my_yats = 18
# using geom_tile
g = ggplot(df, aes(factor(Position, ordered = T)))
g +
coord_cartesian(xlim = c(1, my_xlim)
, ylim = c(0, 6)
, clip = "off") +
geom_bar(aes(fill = group), colour = "grey") +
scale_fill_manual( values = colours
, guide = 'none') +
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
, fill = df$lab_bg) +
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
, fill = df$lab_bg2) +
# Here it's important to specify that your axis goes from 1 to max number of levels
theme( axis.text.x = element_text(size = my_xats
, angle = 90
, hjust = 1
, vjust = 0.4
, colour = my_axis_colours)
, axis.text.y = element_text(size = my_yats
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.x = element_text(size = my_xals)
, axis.title.y = element_text(size = my_yals )
, axis.ticks.x = element_blank()
) +
labs(title = my_title
, x = "Position"
, y = "Frequency")
#========================
# output plot as svg/png
#========================
class(df$lab_bg)
# make this a named vector
# define cartesian coord
my_xlim = length(unique(df$Position)); my_xlim
# axis label size
my_xals = 18
my_yals = 18
# axes text size
my_xats = 14
my_yats = 18
# set output dir for plots
#getwd()
#setwd("~/git/Data/pyrazinamide/output/plots")
#getwd()
plot_name = "barplot_LIG_acoloured.svg"
my_plot_name = paste0(out_dir, "/", plot_name); my_plot_name
svg(my_plot_name, width = 26, height = 4)
g = ggplot(df, aes(factor(Position, ordered = T)))
outFile = g +
coord_cartesian(xlim = c(1, my_xlim)
, ylim = c(0, 6)
, clip = "off"
) +
geom_bar(aes(fill = group), colour = "grey") +
scale_fill_manual( values = colours
, guide = 'none') +
#  geom_tile(aes(,-0.6, width = 0.9, height = 0.7)
#            , fill = df$lab_bg) +
#  geom_tile(aes(,-1, width = 0.9, height = 0.3)
#            , fill = df$lab_bg2) +
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
, fill = df$lab_bg) +
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
, fill = df$lab_bg2) +
# Here it's important to specify that your axis goes from 1 to max number of levels
theme( axis.text.x = element_text(size = my_xats
, angle = 90
, hjust = 1
, vjust = 0.4
, colour = my_axis_colours)
, axis.text.y = element_text(size = my_yats
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.x = element_text(size = my_xals)
, axis.title.y = element_text(size = my_yals )
, axis.ticks.x = element_blank()
) +
labs(title = ""
, x = "Position"
, y = "Frequency")
print(outFile)
dev.off()
# for sanity and good practice
#rm(df)