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