212 lines
6.1 KiB
R
212 lines
6.1 KiB
R
getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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#########################################################
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# TASK:
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#########################################################
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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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# 2: Read file: struct params 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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source("subcols_axis_PS.R")
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# this should return
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# mut_pos_cols
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# my_df
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# my_df_u: df with unique mutations
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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$lab_fg
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# now clear mut_pos_cols
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rm(mut_pos_cols)
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###########################
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# 2: Plot: DUET scores
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###########################
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#==========================
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# Plot 2: Barplot with scores (unordered)
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# corresponds to duet_outcome
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# Stacked Barplot with colours: duet_outcome @ position coloured by
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# stability scores. This is a barplot where each bar corresponds
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# to a SNP and is coloured by its corresponding DUET stability value.
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# Normalised values (range between -1 and 1 ) to aid visualisation
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# NOTE: since barplot plots discrete values, colour = score, so number of
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# colours will be equal to the no. of unique normalised scores
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# rather than a continuous scale
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# will require generating the colour scale separately.
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#============================
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# sanity checks
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upos = unique(my_df$position)
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table(my_df$duet_outcome)
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table(my_df_u$duet_outcome)
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#===========================
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# Data preparation for plots
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#===========================
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# REASSIGNMENT as necessary
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df <- my_df_u
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rm(my_df, my_df_u)
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# add frequency of positions
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library(data.table)
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setDT(df)[, pos_count := .N, by = .(position)]
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# this is cummulative
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table(df$pos_count)
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# use group by on this
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library(dplyr)
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snpsBYpos_df <- df %>%
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group_by(position) %>%
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summarize(snpsBYpos = mean(pos_count))
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table(snpsBYpos_df$snpsBYpos)
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snp_count = sort(unique(snpsBYpos_df$snpsBYpos))
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# sanity checks
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# should be a factor
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is.factor(df$duet_outcome)
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#TRUE
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table(df$duet_outcome)
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# should be -1 and 1
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min(df$duet_scaled)
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max(df$duet_scaled)
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# sanity checks
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# very important!!!!
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tapply(df$duet_scaled, df$duet_outcome, min)
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tapply(df$duet_scaled, df$duet_outcome, max)
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# My colour FUNCTION: based on group and subgroup
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# in my case;
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# df = df
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# group = duet_outcome
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# subgroup = normalised score i.e duet_scaled
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# check unique values in normalised data
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u = unique(df$duet_scaled)
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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$duet_scaledR = round(df$duet_scaled, n)
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ur = unique(df$duet_scaledR)
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# create an extra column called group which contains the "gp name and score"
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# so colours can be generated for each unique values in this column
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#my_grp = df$duet_scaledR # rounding
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my_grp = df$duet_scaled # no rounding
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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df$group <- paste0(df$duet_outcome, "_", my_grp, sep = "")
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# Call the function to create the palette based on the group defined above
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colours <- ColourPalleteMulti(df, "duet_outcome", "my_grp")
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print(paste0("Colour palette generated for: ", length(colours), " colours"))
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my_title = "Protein stability (DUET)"
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#========================
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# plot with axis colours
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#========================
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class(df$lab_bg)
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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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#******************
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# generate plot: with axis colours
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#******************
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# plot name and location
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# outdir/ (should be imported from reading file)
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print(paste0("plot will be in:", outdir))
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bp_aa_subcols_duet = "barplot_acoloured_PS.svg"
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plot_bp_aa_subcols_duet = paste0(outdir, "/plots/", bp_aa_subcols_duet)
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print(paste0("plot name:", plot_bp_aa_subcols_duet))
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svg(plot_bp_aa_subcols_duet, width = 26, height = 4)
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g = ggplot(df, aes(factor(position, ordered = T)))
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outPlot = g +
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coord_cartesian(xlim = c(1, my_xlim)
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#, ylim = c(0, 6)
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, ylim = c(0, max(snp_count))
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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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labs(title = ""
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, x = "position"
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, y = "Frequency")
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print(outPlot)
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dev.off()
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#!!!!!!!!!!!!!!!!
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#Warning message:
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# Vectorized input to `element_text()` is not officially supported.
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#Results may be unexpected or may change in future versions of ggplot2.
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#!!!!!!!!!!!!!!!!!
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# for sanity and good practice
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#rm(df)
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