tanu/LSHTM_analysis
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

updated figure for multi mut plot

Browse source
This commit is contained in:
Tanushree Tunstall 2020-09-11 19:30:20 +01:00
parent 968b57105f
commit e1da853cf1
2 changed files with 112 additions and 128 deletions
Download patch file Download diff file Expand all files Collapse all files

233
scripts/plotting/logo_multiple_muts.R
View file

@ -1,177 +1,149 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: producing logo-type plot showing
# multiple muts per position coloured by aa property
#########################################################
#=======================================================================
# working dir and loading libraries
getwd() getwd()
#setwd("~/Documents/git/LSHTM_Y1_PNCA/combined_v3/logo_plot") # wor_mychisqk setwd("~/git/LSHTM_analysis/scripts/plotting")
setwd("~/git/LSHTM_Y1_PNCA/combined_v3/logo_plot") # thinkpad
#setwd("/Users/tanu/git/LSHTM_Y1_PNCA/combined_v3/logo_plot") # mac
getwd() getwd()
######################################################### source("Header_TT.R")
# 1: Installing and loading required packages #library(ggplot2)
######################################################### #library(data.table)
#library(dplyr)
source("../../Header_TT.R") #===========
# input
#===========
source("combining_dfs_plotting.R")
#source("barplot_colour_function.R") #===========
# output
#===========
#install.packages("ggseqlogo") logo_multiple_muts = "logo_multiple_muts.svg"
plot_logo_multiple_muts = paste0(plotdir,"/", logo_multiple_muts)
library(ggseqlogo) ##########################################################################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
########################################################################
# end of loading libraries and functions #
########################################################################
setwd("/home/tanu/git/LSHTM_analysis/plotting_test")
source("../scripts/plotting/combining_dfs_plotting.R")
# since we will be using df without NA, its best to delete the ones with NA
rm(merged_df2, merged_df3)
###########################
# 3: Data for_mychisq DUET plots
# you need merged_df3_comp
# since these have unique SNPs
###########################
#<<<<<<<<<<<<<<<<<<<<<<<<<
# REASSIGNMENT # REASSIGNMENT
my_df = merged_df3_comp my_df = merged_df3
my_df = merged_df3 #try! #%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#<<<<<<<<<<<<<<<<<<<<<<<<<
# clear excess variables
rm(merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig
, merged_df3_comp, merged_df3_comp_lig
, my_df_u, my_df_u_lig, merged_df3_lig)
colnames(my_df) colnames(my_df)
str(my_df) str(my_df)
rownames(my_df) = my_df$Mutationinfor_mychisqmation #rownames(my_df) = my_df$mutation
c1 = unique(my_df$position) #96 c1 = unique(my_df$position)
nrow(my_df) #189 nrow(my_df)
# get freq count of positions so you can subset freq<1 # get freq count of positions so you can subset freq<1
require(data.table) #require(data.table)
setDT(my_df)[, occurrence := .N, by = .(position)] #189, 36 setDT(my_df)[, mut_pos_occurrence := .N, by = .(position)] #189, 36
table(my_df$position); table(my_df$occurrence) table(my_df$position)
table(my_df$mut_pos_occurrence)
max_mut = max(table(my_df$position))
# extract freq_pos>1 # extract freq_pos>1
my_data_snp = my_df[my_df$occurrence!=1,] #144, 36 my_data_snp = my_df[my_df$mut_pos_occurrence!=1,]
u = unique(my_data_snp$position) #51 u = unique(my_data_snp$position)
max_mult_mut = max(table(my_data_snp$position))
if (nrow(my_data_snp) == nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]] ){
cat("PASS: positions with multiple muts extracted"
, "\nNo. of mutations:", nrow(my_data_snp)
, "\nNo. of positions:", length(u)
, "\nMax no. of muts at any position", max_mult_mut)
}else{
cat("FAIL: positions with multiple muts could NOT be extracted"
, "\nExpected:",nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]]
, "\nGot:", nrow(my_data_snp) )
}
cat("\nNo. of sites with only 1 mutations:", table(my_df$mut_pos_occurrence)[[1]])
######################################################################## ########################################################################
# end of data extraction and cleaning for_mychisq plots # # end of data extraction and cleaning for_mychisq plots #
######################################################################## ########################################################################
#########################################################
#Task: To generate a logo plot or_mychisq bar plot but coloured
#aa properties.
#step1: read mcsm file and or_mychisq file
#step2: plot wild type positions
#step3: plot mutants per position coloured by aa properties
#step4: make the size of the letters/bars prop to or_mychisq if you can!
#########################################################
##useful links
#https://stackoverflow.com/questions/5438474/plotting-a-sequence-logo-using-ggplot2
#https://omarwagih.github.io/ggseqlogo/
#https://kkdey.github.io/Logolas-pages/wor_mychisqkflow.html
#A new sequence logo plot to highlight enrichment and depletion.
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288878/
##very good: http://www.cbs.dtu.dk/biotools/Seq2Logo-2.0/
#############
#PLOTS: Bar plot with aa properties
#using gglogo
#useful links: https://stackoverflow.com/questions/5438474/plotting-a-sequence-logo-using-ggplot2
#############
#following example
require(ggplot2)
require(reshape2)
library(gglogo)
library(ggrepel)
#lmf <- melt(logodf, id.var='pos')
foo = my_data_snp[, c("position", "mutant_type","duet_scaled", "or_mychisq", "mut_prop_polarity", "mut_prop_water") ]
#144, 6
head(foo)
foo = foo[or_mychisqder(foo$position),]
head(foo)
#============== #==============
# matrix for_mychisq mutant type # matrix for_mychisq mutant type
# frequency of mutant type by position # frequency of mutant type by position
#============== #==============
table(my_data_snp$mutant_type, my_data_snp$position) table(my_data_snp$mutant_type, my_data_snp$position)
tab = table(my_data_snp$mutant_type, my_data_snp$position) tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
class(tab) class(tab_mt)
# unclass to convert to matrix
tab = unclass(tab)
tab = as.matrix(tab, rownames = T)
#should be TRUE
is.matrix(tab)
rownames(tab) #aa # unclass to convert to matrix
colnames(tab) #pos tab_mt = unclass(tab_mt)
tab_mt = as.matrix(tab_mt, rownames = T)
#should be TRUE
is.matrix(tab_mt)
rownames(tab_mt) #aa
colnames(tab_mt) #pos
#************** #**************
# Plot 1: mutant logo # Plot 1: mutant logo
#************** #**************
# generate seq logo p0 = ggseqlogo(tab_mt
p0 = ggseqlogo(tab
, method = 'custom' , method = 'custom'
, seq_type = 'aa' , seq_type = 'aa') +
#, col_scheme = "taylor_mychisq"
#, col_scheme = "chemistry2"
) +
#ylab('my custom height') + #ylab('my custom height') +
theme(axis.text.x = element_blank()) + theme(axis.text.x = element_blank()) +
theme_logo()+ theme_logo()+
# scale_x_continuous(breaks=1:51, parse (text = colnames(tab)) ) scale_x_continuous(breaks = 1:ncol(tab_mt)
scale_x_continuous(breaks = 1:ncol(tab) , labels = colnames(tab_mt))+
, labels = colnames(tab))+ scale_y_continuous( breaks = 1:max_mult_mut
scale_y_continuous( breaks = 1:5 , limits = c(0, max_mult_mut))
, limits = c(0, 6))
p0 p0
# further customisation # further customisation
p1 = p0 + theme(legend.position = "none"
p1 = p0 + theme(legend.position = "bottom"
, legend.title = element_blank() , legend.title = element_blank()
, legend.text = element_text(size = 20) , legend.text = element_text(size = 20)
, axis.text.x = element_text(size = 20, angle = 90) , axis.text.x = element_text(size = 20, angle = 90)
, axis.text.y = element_text(size = 20, angle = 90)) , axis.text.y = element_blank())
p1 p1
#============== #==============
# matrix for_mychisq wild type # matrix for wild type
# frequency of wild type by position # frequency of wild type by position
#============== #==============
tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt #17, 51 tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
tab_wt = unclass(tab_wt) tab_wt = unclass(tab_wt)
#remove wt duplicates #remove wt duplicates
wt = my_data_snp[, c("position", "wild_type")] #144, 2 wt = my_data_snp[, c("position", "wild_type")]
wt = wt[!duplicated(wt),]#51, 2 wt = wt[!duplicated(wt),]
tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1 tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
rownames(tab_wt) rownames(tab_wt)
rownames(tab) rownames(tab_wt)
#************** #**************
# Plot 2: for_mychisq wild_type # Plot 2: wild_type logo
# with custom x axis to reflect my aa positions
#************** #**************
# sanity check: MUST BE TRUE # sanity check: MUST BE TRUE
# for_mychisq the cor_mychisqrectnes of the x axis
identical(colnames(tab), colnames(tab_wt)) identical(colnames(tab_mt), colnames(tab_wt))
identical(ncol(tab), ncol(tab_wt)) identical(ncol(tab_mt), ncol(tab_wt))
p2 = ggseqlogo(tab_wt p2 = ggseqlogo(tab_wt
, method = 'custom' , method = 'custom'
@ -184,16 +156,20 @@ p2 = ggseqlogo(tab_wt
, axis.text.y = element_blank()) + , axis.text.y = element_blank()) +
theme_logo() + theme_logo() +
scale_x_continuous(breaks = 1:ncol(tab_wt) scale_x_continuous(breaks = 1:ncol(tab_wt)
, labels = colnames(tab_wt)) + , labels = colnames(tab_wt))
scale_y_continuous( limits = c(0, 5))
p2 p2
# further customise
# further customise
p3 = p2 + p3 = p2 +
theme(legend.position = "none" theme(legend.position = "bottom"
, axis.text.x = element_text(size = 20 #, legend.title = element_blank()
, angle = 90) , legend.title = element_text("Amino acid properties", size = 20)
, axis.text.y = element_blank()) , legend.text = element_text( size = 20)
, axis.text.x = element_text(size = 20, angle = 90)
, axis.text.y = element_blank()
, axis.title.x = element_text(size = 22))+
labs(x= "Position")
p3 p3
@ -202,11 +178,18 @@ p3
suppressMessages( require(cowplot) ) suppressMessages( require(cowplot) )
plot_grid(p1, p3, ncol = 1, align = 'v') #+ plot_grid(p1, p3, ncol = 1, align = 'v') #+
# background_grid(minor_mychisq = "xy"
# , size.minor_mychisq = 1
# , colour.minor_mychisq = "grey86")
#colour scheme #colour scheme
#https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r #https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
cat("Output plot:", plot_logo_multiple_muts)
svg(plot_logo_multiple_muts, width = 32, height = 10)
OutPlot1 = cowplot::plot_grid(p1, p3
, nrow = 2
, align = "v"
, rel_heights = c(3/4, 1/4))
print(OutPlot1)
dev.off()

3
scripts/plotting/logo_plot.R
View file

@ -1,6 +1,7 @@
#!/usr/bin/env Rscript #!/usr/bin/env Rscript
######################################################### #########################################################
# TASK: producing boxplots for dr and other muts # TASK: producing logoplot
# from data and/or from sequence
######################################################### #########################################################
#======================================================================= #=======================================================================