added subaxis plots for PS and lig separately
This commit is contained in:
parent
6cbef0c3d7
commit
ac34de9e79
4 changed files with 601 additions and 81 deletions
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@ -1,38 +1,18 @@
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getwd()
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max(my_df2$Dis_lig_Ang) #9.847
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setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
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min(my_df2$Dis_lig_Ang) #3.047
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getwd()
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# count no of unique positions
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source("../Header_TT.R")
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length(unique(my_df2$Position))#47
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source("../barplot_colour_function.R")
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#count no of unique mutations
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############################################################
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length(unique(my_df2$Mutationinformation)) #110
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# Output dir for plots
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#clear variable to avoid confusion
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############################################################
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rm(my_df)
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out_dir = "~/git/Data/pyrazinamide/output/plots"
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#%%%%%%%%%%
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source("subcols_axis.R")
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# Reassign
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table(mut_pos_cols$lab_bg)
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#%%%%%%%%%%
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#blue cornflowerblue green purple white yellow
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my_df = my_df2
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#2 2 2 4 117 3
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# Stacked Barplot with colours: Lig_outcome @ position coloured by
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sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should be True
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table(mut_pos_cols$lab_bg2)
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#green white
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#2 128
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sum( table(mut_pos_cols$lab_bg2) ) == nrow(mut_pos_cols) # should be True
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table(mut_pos_cols$lab_fg)
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#black white
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#124 6
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sum( table(mut_pos_cols$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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###########################
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# 2: Plot: DUET 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 DUET_outcome
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# Stacked Barplot with colours: DUET_outcome @ position coloured by
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# stability scores. This is a barplot where each bar corresponds
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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 DUET stability value.
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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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# 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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# 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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# colours will be equal to the no. of unique normalised scores
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@ -41,7 +21,7 @@ rm(mut_pos_cols)
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#============================
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#============================
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# sanity checks
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# sanity checks
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upos = unique(my_df$Position)
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upos = unique(my_df$Position)
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str(my_df$DUET_outcome)
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str(my_df$Lig_outcome)
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colnames(my_df)
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colnames(my_df)
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#===========================
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#===========================
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# Data preparation for plots
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# Data preparation for plots
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@ -49,44 +29,146 @@ colnames(my_df)
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#!!!!!!!!!!!!!!!!!
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#!!!!!!!!!!!!!!!!!
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# REASSIGNMENT
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# REASSIGNMENT
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df <- my_df
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df <- my_df
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#!!!!!!!!!!!!!!!!!
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rm(my_df)
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rm(my_df)
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# sanity checks
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# sanity checks
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# should be a factor
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# should be a factor
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is.factor(df$DUET_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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table(df$DUET_outcome)
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# sanity checks
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#Destabilizing Stabilizing
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# should be a factor
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#288 47
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is.factor(df$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$Lig_outcome)
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# should be -1 and 1
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# should be -1 and 1
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min(df$ratioDUET)
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min(df$ratioPredAff)
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max(df$ratioDUET)
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max(df$ratioPredAff)
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# sanity checks
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# sanity checks
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# very important!!!!
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# very important!!!!
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tapply(df$ratioDUET, df$DUET_outcome, min)
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tapply(df$ratioPredAff, df$Lig_outcome, min)
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#Destabilizing Stabilizing
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tapply(df$ratioPredAff, df$Lig_outcome, max)
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#-1.0000000 0.01065719
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# sanity checks
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tapply(df$ratioDUET, df$DUET_outcome, max)
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# very important!!!!
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#Destabilizing Stabilizing
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tapply(df$ratioPredAff, df$Lig_outcome, min)
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#-0.003875969 1.0000000
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tapply(df$ratioPredAff, df$Lig_outcome, max)
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# check unique values in normalised data
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# check unique values in normalised data
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u = unique(df$ratioDUET) # 323
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u = unique(df$ratioPredAff)
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# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# Run this section if rounding is to be used
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# Run this section if rounding is to be used
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# specify number for rounding
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# specify number for rounding
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n = 3
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n = 3
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df$ratioDUETR = round(df$ratioDUET, n) # 335, 40
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df$ratioPredAffR = round(df$ratioPredAff, n)
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u = unique(df$ratioDUETR) # 287
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u = unique(df$ratioPredAffR)
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# create an extra column called group which contains the "gp name and score"
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# create an extra column called group which contains the "gp name and score"
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# so colours can be generated for each unique values in this column
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# so colours can be generated for each unique values in this column
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my_grp = df$ratioDUETR
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my_grp = df$ratioPredAffR
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df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "") # 335,41
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df$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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# Call the function to create the palette based on the group defined above
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colours <- ColourPalleteMulti(df, "DUET_outcome", "my_grp")
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colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
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my_title = "Protein stability (DUET)"
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my_title = "Protein stability (PredAff)"
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library(ggplot2)
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library(ggplot2)
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# axis label size
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# axis label size
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my_xaxls = 13
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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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my_yaxls = 15
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# axes text size
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# axes text size
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my_xaxts = 15
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my_xaxts = 15
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@ -129,9 +211,9 @@ coord_cartesian(xlim = c(1, my_xlim)
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geom_bar(aes(fill = group), colour = "grey") +
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geom_bar(aes(fill = group), colour = "grey") +
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scale_fill_manual( values = colours
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scale_fill_manual( values = colours
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, guide = 'none') +
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, guide = 'none') +
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geom_tile(aes(,-0.8, width = 0.9, height = 0.85)
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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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, fill = df$lab_bg) +
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geom_tile(aes(,-1.2, width = 0.9, height = -0.2)
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geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
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, fill = df$lab_bg2) +
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, fill = df$lab_bg2) +
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# Here it's important to specify that your axis goes from 1 to max number of levels
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# Here it's important to specify that your axis goes from 1 to max number of levels
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theme( axis.text.x = element_text(size = my_xats
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theme( axis.text.x = element_text(size = my_xats
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@ -150,6 +232,9 @@ theme( axis.text.x = element_text(size = my_xats
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labs(title = my_title
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labs(title = my_title
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, x = "Position"
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, x = "Position"
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, y = "Frequency")
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, y = "Frequency")
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#========================
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# output plot as svg/png
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#========================
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class(df$lab_bg)
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class(df$lab_bg)
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# make this a named vector
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# make this a named vector
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# define cartesian coord
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# define cartesian coord
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# axes text size
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# axes text size
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my_xats = 14
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my_xats = 14
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my_yats = 18
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my_yats = 18
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my_plot_name = "barplot_PS_acoloured.svg"
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# set output dir for plots
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out_file = paste0(out_dir, "/", my_plot_name); outfile
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#getwd()
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svg(outfile, width = 26, height = 4)
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#setwd("~/git/Data/pyrazinamide/output/plots")
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svg(out_file, width = 26, height = 4)
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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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||||||
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# to a SNP and is coloured by its corresponding PredAff stability value.
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||||||
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# Normalised values (range between -1 and 1 ) to aid visualisation
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||||||
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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$Lig_outcome)
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colnames(my_df)
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#===========================
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# Data preparation for plots
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#===========================
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#!!!!!!!!!!!!!!!!!
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# REASSIGNMENT
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df <- my_df
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#!!!!!!!!!!!!!!!!!
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rm(my_df)
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# sanity checks
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||||||
|
# should be a factor
|
||||||
|
is.factor(df$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
|
# using geom_tile
|
||||||
g = ggplot(df, aes(factor(Position, ordered = T)))
|
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 +
|
outFile = g +
|
||||||
coord_cartesian(xlim = c(1, my_xlim)
|
coord_cartesian(xlim = c(1, my_xlim)
|
||||||
, ylim = c(0, 6)
|
, ylim = c(0, 6)
|
||||||
|
|
@ -178,9 +485,9 @@ scale_fill_manual( values = colours
|
||||||
# , fill = df$lab_bg) +
|
# , fill = df$lab_bg) +
|
||||||
# geom_tile(aes(,-1, width = 0.9, height = 0.3)
|
# geom_tile(aes(,-1, width = 0.9, height = 0.3)
|
||||||
# , fill = df$lab_bg2) +
|
# , fill = df$lab_bg2) +
|
||||||
geom_tile(aes(,-0.8, width = 0.9, height = 0.85)
|
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
|
||||||
, fill = df$lab_bg) +
|
, fill = df$lab_bg) +
|
||||||
geom_tile(aes(,-1.2, width = 0.9, height = -0.2)
|
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
|
||||||
, fill = df$lab_bg2) +
|
, fill = df$lab_bg2) +
|
||||||
# Here it's important to specify that your axis goes from 1 to max number of levels
|
# 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
|
theme( axis.text.x = element_text(size = my_xats
|
||||||
|
|
@ -201,3 +508,5 @@ labs(title = ""
|
||||||
, y = "Frequency")
|
, y = "Frequency")
|
||||||
print(outFile)
|
print(outFile)
|
||||||
dev.off()
|
dev.off()
|
||||||
|
# for sanity and good practice
|
||||||
|
#rm(df)
|
||||||
|
|
|
||||||
|
|
@ -196,9 +196,9 @@ g +
|
||||||
geom_bar(aes(fill = group), colour = "grey") +
|
geom_bar(aes(fill = group), colour = "grey") +
|
||||||
scale_fill_manual( values = colours
|
scale_fill_manual( values = colours
|
||||||
, guide = 'none') +
|
, guide = 'none') +
|
||||||
geom_tile(aes(,-0.8, width = 0.9, height = 0.85)
|
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
|
||||||
, fill = df$lab_bg) +
|
, fill = df$lab_bg) +
|
||||||
geom_tile(aes(,-1.2, width = 0.9, height = -0.2)
|
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
|
||||||
, fill = df$lab_bg2) +
|
, fill = df$lab_bg2) +
|
||||||
|
|
||||||
# Here it's important to specify that your axis goes from 1 to max number of levels
|
# Here it's important to specify that your axis goes from 1 to max number of levels
|
||||||
|
|
@ -261,9 +261,9 @@ outFile = g +
|
||||||
# , fill = df$lab_bg) +
|
# , fill = df$lab_bg) +
|
||||||
# geom_tile(aes(,-1, width = 0.9, height = 0.3)
|
# geom_tile(aes(,-1, width = 0.9, height = 0.3)
|
||||||
# , fill = df$lab_bg2) +
|
# , fill = df$lab_bg2) +
|
||||||
geom_tile(aes(,-0.8, width = 0.9, height = 0.85)
|
geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
|
||||||
, fill = df$lab_bg) +
|
, fill = df$lab_bg) +
|
||||||
geom_tile(aes(,-1.2, width = 0.9, height = -0.2)
|
geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
|
||||||
, fill = df$lab_bg2) +
|
, fill = df$lab_bg2) +
|
||||||
|
|
||||||
# Here it's important to specify that your axis goes from 1 to max number of levels
|
# Here it's important to specify that your axis goes from 1 to max number of levels
|
||||||
|
|
|
||||||
208
mcsm_analysis/pyrazinamide/scripts/plotting/subcols_axis_LIG.R
Normal file
208
mcsm_analysis/pyrazinamide/scripts/plotting/subcols_axis_LIG.R
Normal file
|
|
@ -0,0 +1,208 @@
|
||||||
|
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")
|
||||||
|
#library(tidyverse)
|
||||||
|
|
||||||
|
###########################
|
||||||
|
#2: Read file: normalised file, output of step 4 mcsm pipeline
|
||||||
|
###########################
|
||||||
|
#my_df <- read.csv("../../Data/mcsm_complex1_normalised.csv"
|
||||||
|
# , row.names = 1
|
||||||
|
# , stringsAsFactors = F
|
||||||
|
# , header = T)
|
||||||
|
|
||||||
|
# call script combining_df
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
# from Plotting to Scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA for pyrazinamide:
|
||||||
|
#merged_df2
|
||||||
|
#merged_df2_comp
|
||||||
|
|
||||||
|
# df without NA for pyrazinamide:
|
||||||
|
#merged_df3
|
||||||
|
#merged_df3_comp
|
||||||
|
#==========================
|
||||||
|
###########################
|
||||||
|
# Data to choose:
|
||||||
|
# We will be using the small dfs
|
||||||
|
# to generate the coloured axis
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
str(my_df)
|
||||||
|
my_df$Position
|
||||||
|
c1 = my_df[my_df$Mutationinformation == "A134V",]
|
||||||
|
|
||||||
|
# order my_df by Position
|
||||||
|
my_df_o = my_df[order(my_df$Position),]
|
||||||
|
head(my_df_o$Position); tail(my_df_o$Position)
|
||||||
|
|
||||||
|
c2 = my_df_o[my_df_o$Mutationinformation == "A134V",]
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
if (sum(table(c1 == c2)) == ncol(my_df)){
|
||||||
|
print ("Sanity check passsd")
|
||||||
|
}else{
|
||||||
|
print ("Error!: Please debug your code")
|
||||||
|
}
|
||||||
|
|
||||||
|
rm(my_df, c1, c2)
|
||||||
|
|
||||||
|
# create a new df with unique position numbers and cols
|
||||||
|
Position = unique(my_df_o$Position)
|
||||||
|
Position_cols = as.data.frame(Position)
|
||||||
|
|
||||||
|
head(Position_cols) ; tail(Position_cols)
|
||||||
|
|
||||||
|
# specify active site residues and bg colour
|
||||||
|
Position = c(49, 51, 57, 71
|
||||||
|
, 8, 96, 138
|
||||||
|
, 13, 68
|
||||||
|
, 103, 137
|
||||||
|
, 133, 134) #13
|
||||||
|
|
||||||
|
lab_bg = rep(c("purple"
|
||||||
|
, "yellow"
|
||||||
|
, "cornflowerblue"
|
||||||
|
, "blue"
|
||||||
|
, "green"), times = c(4, 3, 2, 2, 2)
|
||||||
|
)
|
||||||
|
|
||||||
|
# second bg colour for active site residues
|
||||||
|
#lab_bg2 = rep(c("white"
|
||||||
|
# , "green" , "white", "green"
|
||||||
|
# , "white"
|
||||||
|
# , "white"
|
||||||
|
# , "white"), times = c(4
|
||||||
|
# , 1, 1, 1
|
||||||
|
# , 2
|
||||||
|
# , 2
|
||||||
|
# , 2)
|
||||||
|
#)
|
||||||
|
|
||||||
|
#%%%%%%%%%
|
||||||
|
# revised: leave the second box coloured as the first one incase there is no second colour
|
||||||
|
#%%%%%%%%%
|
||||||
|
lab_bg2 = rep(c("purple"
|
||||||
|
, "green", "yellow", "green"
|
||||||
|
, "cornflowerblue"
|
||||||
|
, "blue"
|
||||||
|
, "green"), times = c(4
|
||||||
|
, 1, 1, 1
|
||||||
|
, 2
|
||||||
|
, 2
|
||||||
|
, 2))
|
||||||
|
|
||||||
|
# fg colour for labels for active site residues
|
||||||
|
lab_fg = rep(c("white"
|
||||||
|
, "black"
|
||||||
|
, "black"
|
||||||
|
, "white"
|
||||||
|
, "black"), times = c(4, 3, 2, 2, 2))
|
||||||
|
|
||||||
|
#%%%%%%%%%
|
||||||
|
# revised: make the purple ones black
|
||||||
|
# fg colour for labels for active site residues
|
||||||
|
#%%%%%%%%%
|
||||||
|
#lab_fg = rep(c("black"
|
||||||
|
# , "black"
|
||||||
|
# , "black"
|
||||||
|
# , "white"
|
||||||
|
# , "black"), times = c(4, 3, 2, 2, 2))
|
||||||
|
|
||||||
|
# combined df with active sites, bg and fg colours
|
||||||
|
aa_cols_ref = data.frame(Position
|
||||||
|
, lab_bg
|
||||||
|
, lab_bg2
|
||||||
|
, lab_fg
|
||||||
|
, stringsAsFactors = F) #13, 4
|
||||||
|
|
||||||
|
str(Position_cols); class(Position_cols)
|
||||||
|
str(aa_cols_ref); class(aa_cols_ref)
|
||||||
|
|
||||||
|
# since Position is int and numeric in the two dfs resp,
|
||||||
|
# converting numeric to int for consistency
|
||||||
|
aa_cols_ref$Position = as.integer(aa_cols_ref$Position)
|
||||||
|
class(aa_cols_ref$Position)
|
||||||
|
|
||||||
|
#===========
|
||||||
|
# Merge 1: merging Positions df (Position_cols) and
|
||||||
|
# active site cols (aa_cols_ref)
|
||||||
|
# linking column: "Position"
|
||||||
|
# This is so you can have colours defined for all 130 positions
|
||||||
|
#===========
|
||||||
|
head(Position_cols$Position); head(aa_cols_ref$Position)
|
||||||
|
|
||||||
|
mut_pos_cols = merge(Position_cols, aa_cols_ref
|
||||||
|
, by = "Position"
|
||||||
|
, all.x = TRUE)
|
||||||
|
|
||||||
|
head(mut_pos_cols)
|
||||||
|
# replace NA's
|
||||||
|
# :column "lab_bg" with "white"
|
||||||
|
# : column "lab_fg" with "black"
|
||||||
|
mut_pos_cols$lab_bg[is.na(mut_pos_cols$lab_bg)] <- "white"
|
||||||
|
mut_pos_cols$lab_bg2[is.na(mut_pos_cols$lab_bg2)] <- "white"
|
||||||
|
mut_pos_cols$lab_fg[is.na(mut_pos_cols$lab_fg)] <- "black"
|
||||||
|
head(mut_pos_cols)
|
||||||
|
|
||||||
|
#===========
|
||||||
|
# Merge 2: Merge mut_pos_cols with mcsm df
|
||||||
|
# Now combined the 130 positions with aa colours with
|
||||||
|
# the mcsm_data
|
||||||
|
#===========
|
||||||
|
# dfs to merge
|
||||||
|
df0 = my_df_o
|
||||||
|
df1 = mut_pos_cols
|
||||||
|
|
||||||
|
# check the column on which merge will be performed
|
||||||
|
head(df0$Position); tail(df0$Position)
|
||||||
|
head(df1$Position); tail(df1$Position)
|
||||||
|
|
||||||
|
# should now have 3 extra columns
|
||||||
|
my_df = merge(df0, df1
|
||||||
|
, by = "Position"
|
||||||
|
, all.x = TRUE)
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
my_df[my_df$Position == "49",]
|
||||||
|
my_df[my_df$Position == "13",]
|
||||||
|
|
||||||
|
my_df$Position
|
||||||
|
|
||||||
|
# clear variables
|
||||||
|
rm(aa_cols_ref
|
||||||
|
, df0
|
||||||
|
, df1
|
||||||
|
, my_df_o
|
||||||
|
, Position_cols
|
||||||
|
, lab_bg
|
||||||
|
, lab_bg2
|
||||||
|
, lab_fg
|
||||||
|
, Position
|
||||||
|
)
|
||||||
|
|
||||||
|
|
@ -104,32 +104,35 @@ lab_bg = rep(c("purple"
|
||||||
# , 2)
|
# , 2)
|
||||||
#)
|
#)
|
||||||
|
|
||||||
|
#%%%%%%%%%
|
||||||
# revised: leave the second box coloured as the first one incase there is no second colour
|
# revised: leave the second box coloured as the first one incase there is no second colour
|
||||||
|
#%%%%%%%%%
|
||||||
lab_bg2 = rep(c("purple"
|
lab_bg2 = rep(c("purple"
|
||||||
, "green" , "yellow", "green"
|
, "green", "yellow", "green"
|
||||||
, "cornflowerblue"
|
, "cornflowerblue"
|
||||||
, "blue"
|
, "blue"
|
||||||
, "green"), times = c(4
|
, "green"), times = c(4
|
||||||
, 1, 1, 1
|
, 1, 1, 1
|
||||||
, 2
|
, 2
|
||||||
, 2
|
, 2
|
||||||
, 2)
|
, 2))
|
||||||
)
|
|
||||||
|
|
||||||
# fg colour for labels for active site residues
|
# fg colour for labels for active site residues
|
||||||
#lab_fg = rep(c("white"
|
lab_fg = rep(c("white"
|
||||||
# , "black"
|
|
||||||
# , "black"
|
|
||||||
# , "white"
|
|
||||||
# , "black"), times = c(4, 3, 2, 2, 2))
|
|
||||||
|
|
||||||
# revised: make the purple ones black
|
|
||||||
# fg colour for labels for active site residues
|
|
||||||
lab_fg = rep(c("black"
|
|
||||||
, "black"
|
, "black"
|
||||||
, "black"
|
, "black"
|
||||||
, "white"
|
, "white"
|
||||||
, "black"), times = c(4, 3, 2, 2, 2))
|
, "black"), times = c(4, 3, 2, 2, 2))
|
||||||
|
|
||||||
|
#%%%%%%%%%
|
||||||
|
# revised: make the purple ones black
|
||||||
|
# fg colour for labels for active site residues
|
||||||
|
#%%%%%%%%%
|
||||||
|
#lab_fg = rep(c("black"
|
||||||
|
# , "black"
|
||||||
|
# , "black"
|
||||||
|
# , "white"
|
||||||
|
# , "black"), times = c(4, 3, 2, 2, 2))
|
||||||
|
|
||||||
# combined df with active sites, bg and fg colours
|
# combined df with active sites, bg and fg colours
|
||||||
aa_cols_ref = data.frame(Position
|
aa_cols_ref = data.frame(Position
|
||||||
Loading…
Add table
Add a link
Reference in a new issue