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LSHTM_analysis/scripts/plotting/logo_plot.R

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#!/usr/bin/env Rscript
#########################################################
# TASK: producing logoplot
# from data and/or from sequence
#########################################################
#=======================================================================
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()
source("Header_TT.R")
#library(ggplot2)
#library(data.table)
#library(dplyr)
#===========
# input
#===========
source("combining_dfs_plotting.R")
#===========
# output
#===========
logo_plot = "logo_plot.svg"
plot_logo_plot = paste0(plotdir,"/", logo_plot)
#==========================
# This will return:
# df with NA for pyrazinamide:
# merged_df2
# merged_df3
# df without NA for pyrazinamide:
# merged_df2_comp
# merged_df3_comp
#===========================
###########################
# Data for plots
# you need merged_df2 or merged_df2_comp
# since this is one-many relationship
# i.e the same SNP can belong to multiple lineages
# using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available, hence use df with NA
# This will the first plotting df
# Then subset this to extract dr muts only (second plottig df)
###########################
#%%%%%%%%%%%%%%%%%%%%%%%%%
# uncomment as necessary
# REASSIGNMENT
#my_data = merged_df2
#my_data = merged_df2_comp
my_data = merged_df3
my_data = merged_df3_comp
#%%%%%%%%%%%%%%%%%%%%%%%%%%
# delete variables not required
rm(merged_df2, merged_df2_comp)
#rm(merged_df3, merged_df3_comp)
# quick checks
colnames(my_data)
str(my_data)
c1 = unique(my_data$position)
nrow(my_data)
cat("No. of rows in my_data:", nrow(my_data)
, "\nDistinct positions corresponding to snps:", length(c1)
, "\n===========================================================")
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# FIXME: Think and decide what you want to remove
# mut_pos_occurence < 1 or sample_pos_occurrence <1
# get freq count of positions so you can subset freq<1
require(data.table)
#setDT(my_data)[, mut_pos_occurrence := .N, by = .(position)] #265, 14
#extract freq_pos>1
#my_data_snp = my_data[my_data$occurrence!=1,]
#u = unique(my_data_snp$position) #73
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT to prevent changing code
my_data_snp = my_data
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#=======================================================================
#%% logo plots from dataframe
#############
# PLOTS: ggseqlogo with custom height
# https://omarwagih.github.io/ggseqlogo/
#############
#require(ggplot2)
#require(tidyverse)
library(ggseqlogo)
foo = my_data_snp[, c("position", "mutant_type","duet_scaled", "or_mychisq"
, "mut_prop_polarity", "mut_prop_water") ]
my_data_snp$log10or = log10(my_data_snp$or_mychisq)
logo_data = my_data_snp[, c("position", "mutant_type", "or_mychisq", "log10or")]
logo_data_or = my_data_snp[, c("position", "mutant_type", "or_mychisq")]
wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = 0.0)
wide_df_or = as.matrix(wide_df_or)
rownames(wide_df_or) = wide_df_or[,1]
wide_df_or = wide_df_or[,-1]
str(wide_df_or)
position_or = as.numeric(colnames(wide_df_or))
#===========================================
#custom height (OR) logo plot: CORRECT x-axis labelling
#============================================
# custom height (OR) logo plot: yayy works
cat("Logo plot with OR as y axis:", plot_logo_plot)
svg(plot_logo_plot, width = 30 , height = 6)
logo_or = ggseqlogo(wide_df_or, method="custom", seq_type="aa") + ylab("my custom height") +
theme( axis.text.x = element_text(size = 16
, angle = 90
, hjust = 1
, vjust = 0.4)
, axis.text.y = element_text(size = 22
, angle = 0
, hjust = 1
, vjust = 0)
, axis.title.y = element_text(size = 25)
, axis.title.x = element_text(size = 20)
#, legend.position = "bottom") +
, legend.position = "none")+
#, legend.text = element_text(size = 15)
#, legend.title = element_text(size = 15))+
scale_x_discrete("Position"
#, breaks
, labels = position_or
, limits = factor(1:length(position_or))) +
ylab("Odds Ratio")
print(logo_or)
dev.off()
#%% end of logo plot with OR as height
#=======================================================================
# extracting data with log10R
logo_data_logor = my_data_snp[, c("position", "mutant_type", "log10or")]
wide_df_logor <- logo_data_logor %>% spread(position, log10or, fill = 0.0)
wide_df_logor = as.matrix(wide_df_logor)
rownames(wide_df_logor) = wide_df_logor[,1]
wide_df_logor = subset(wide_df_logor, select = -c(1) )
colnames(wide_df_logor)
wide_df_logor_m = data.matrix(wide_df_logor)
rownames(wide_df_logor_m)
colnames(wide_df_logor_m)
# FIXME
#my_ylim_up = as.numeric(max(wide_df_logor_m)) * 5
#my_ylim_low = as.numeric(min(wide_df_logor_m))
position_logor = as.numeric(colnames(wide_df_logor_m))
# custom height (log10OR) logo plot: yayy works
ggseqlogo(wide_df_logor_m, method="custom", seq_type="aa") + ylab("my custom height") +
theme(legend.position = "bottom"
, axis.text.x = element_text(size = 11
, angle = 90
, hjust = 1
, vjust = 0.4)
, axis.text.y = element_text(size = 15
, angle = 0
, hjust = 1
, vjust = 0))+
scale_x_discrete("Position"
#, breaks
, labels = position_logor
, limits = factor(1:length(position_logor)))+
ylab("Log (Odds Ratio)") +
scale_y_continuous(limits = c(0, 9))
#=======================================================================
#%% logo plot from sequence
#################
# Plot: LOGOLAS (ED plots)
# link: https://github.com/kkdey/Logolas
# on all pncA samples: output of mutate.py
################
library(Logolas)
# data was pnca_msa.txt
#FIXME: generate this file
seqs = read.csv("~/git/Data/pyrazinamide/output/pnca_msa.txt"
, header = FALSE
, stringsAsFactors = FALSE)$V1
# my_data: useful!
logomaker(seqs, type = "EDLogo", color_type = "per_symbol"
, return_heights = TRUE)
logomaker(seqs, type = "Logo", color_type = "per_symbol", return_heights = TRUE)
#%% end of script
#=======================================================================
#==============
# online logo:
#http://www.cbs.dtu.dk/biotools/Seq2Logo/ # good for getting pssm and psfm matrices
#https://weblogo.berkeley.edu/logo.cgi
#http://weblogo.threeplusone.com/create.cgi # weblogo3
#===============
# PSSM logos= example from logomaker
#===============
data(pssm)
logo_pssm(pssm, control = list(round_off = 0))
#=================
# PSSM: output from http://www.cbs.dtu.dk/biotools/Seq2Logo/
# of MSA: pnca_msa.txt
#==================
foo = read.csv("/home/tanu/git/Data/pyrazinamide/pssm_transpose.csv")
rownames(foo) = foo[,1]
df = subset(foo, select = -c(1) )
colnames(df)
colnames(df) = seq(1:length(df))
df_m = as.matrix(df)
logo_pssm(df_m, control = list(round_off = 0))
#=================
# # PSFM: output from http://www.cbs.dtu.dk/biotools/Seq2Logo/
# of MSA: pnca_msa.txt
#=================
# not designed for PSFM!
# may want to figure out how to do it!
logo_data = read.csv("/home/tanu/git/Data/pyrazinamide/psfm_transpose.csv")
rownames(logo_data) = logo_data[,1]
df2 = subset(logo_data, select = -c(1) )
colnames(df2)
colnames(df2) = seq(1:length(df2))
df2_m = as.matrix(df2)
logo_pssm(df2_m, control = list(round_off = 0))
#=================
# ggplots:
#https://stackoverflow.com/questions/5438474/plotting-a-sequence-logo-using-ggplot2
#=================
library(ggplot2)
library(gglogo)
ggplot(data = ggfortify(sequences, "peptide")) +
geom_logo(aes(x=position, y=bits, group=element,
label=element, fill=interaction(Polarity, Water)),
alpha = 0.6) +
scale_fill_brewer(palette="Paired") +
theme(legend.position = "bottom")
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