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Data_Visualization.R
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Data_Visualization.R
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###############################################################
### Data Visualization ###
###############################################################
setwd('~/bigdata/LABDATA/BLUPHAT/')
library(ggplot2)
##############################################################
############# Figure 1, 5, 6 Survival Analyses
# Code for plottting results of survival analyses are in the corresponding scripts
# Figure 1: RFS_Survival_Analysis_Nomogram.R
# Figure 5: SFS-BLUPHAT_MultiOmics_RFS.R
# Figure 6: SFS-BLUPHAT_MultiOmics_RFS.R
##############################################################
############# Figure 2: GS Model Evaluation
files <- file.path('report/GS_Omics_Comparison', dir('report/GS_Omics_Comparison'))
files
BLUP <- c()
LASSO <- c()
PLS <- c()
BayesB <- c()
SVMRBF <- c()
SVMPOLY <- c()
for (fl in files) {
hat <- read.table(fl, sep='\t', stringsAsFactors = F)
BLUP <- c(BLUP, as.numeric(hat[11,1]))
LASSO <- c(LASSO, as.numeric(hat[11,2]))
PLS <- c(PLS, as.numeric(hat[11,3]))
BayesB <- c(BayesB, as.numeric(hat[11,4]))
SVMRBF <- c(SVMRBF, as.numeric(hat[11,5]))
SVMPOLY <- c(SVMPOLY, as.numeric(hat[11,6]))
}
method <- c('BLUP', 'LASSO', 'PLS', 'BayesB', 'SVM-RBF', 'SVM-POLY')
omics <- c('TR', 'MI', 'ME', 'TR+MI', 'TR+ME', 'MI+ME', 'TR+MI+ME')
trait <- c('PFR5YR', 'PFR10YR', 'OCD', 'ECE', 'LNI', 'SVI')
HAT <- data.frame(Predictability=c(BLUP, LASSO, PLS, BayesB, SVMRBF, SVMPOLY)^2,
Models = factor(rep(method, each=length(BLUP)), levels=method),
Omics=factor(rep(c('ME','MI','MI+ME','TR','TR+ME','TR+MI','TR+MI+ME'),each=6),
levels=omics),
Traits=factor(rep(c('ECE','LNI','OCD','PFR10YR','PFR5YR','SVI'),7), levels=trait))
aggregate(HAT$Predictability, list(HAT$Models), mean)
aggregate(HAT$Predictability, list(HAT$Omics), mean)
aggregate(HAT$Predictability, list(HAT$Traits), mean)
p1 <- ggplot(data=HAT, aes(x=Traits, y=Predictability, color=Traits)) +
#stat_boxplot(geom ='errorbar',width=0.3) +
geom_boxplot() + #ylim(0,0.6) +
stat_summary(fun.y='mean', geom="point", shape=23, size=3) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
#panel.grid.minor = element_blank(),
axis.text = element_text(size=14, color='black'),
axis.title.y = element_text(size=16),
axis.title.x = element_blank(),
legend.text = element_blank(),
legend.position = 'none',
panel.background = element_blank(),
panel.border = element_rect(color='black'))
p1
p2 <- ggplot(data=HAT, aes(x=Omics, y=Predictability, color=Omics)) +
#stat_boxplot(geom ='errorbar',width=0.3) +
geom_boxplot() + #ylim(0,0.6) +
stat_summary(fun.y='mean', geom="point", shape=23, size=3) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
#panel.grid.minor = element_blank(),
axis.text = element_text(size=14, color='black'),
axis.title.y = element_text(size=16),
axis.title.x = element_blank(),
legend.text = element_blank(),
legend.position = 'none',
panel.background = element_blank(),
panel.border = element_rect(color='black'))
p2
p3 <- ggplot(data=HAT, aes(x=Models, y=Predictability, color=Models)) +
#stat_boxplot(geom ='errorbar',width=0.3) +
geom_boxplot() + #ylim(0,0.6) +
stat_summary(fun.y='mean', geom="point", shape=23, size=3) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
#panel.grid.minor = element_blank(),
axis.text = element_text(size=14, color='black'),
axis.title.y = element_text(size=16),
axis.title.x = element_blank(),
legend.text = element_blank(),
legend.position = 'none',
panel.background = element_blank(),
panel.border = element_rect(color='black'))
p3
comp <- list(BLUP, LASSO, PLS, BayesB, SVMRBF, SVMPOLY)
lapply(comp, mean)
multiplot(p1, p2, p3) # 800*600
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
##############################################################
############# Figure 3: Hypothesis Tests
### RNAseq
files <-file.path('report/HAT_Nomogram', dir('report/HAT_Nomogram/'))
idx <- grep(pattern = 'Step1', x = files, fixed = T)
files <- files[idx]
files
com <- NULL
tr <- NULL
rand <- NULL
for (fl in files) {
hat <- read.delim(fl, stringsAsFactors = F)
rand <- rbind(rand, hat[1:3,])
com <- rbind(com, hat[4:6,])
tr <- rbind(tr, hat[10:nrow(hat),])
}
tr$x <- as.numeric(tr$x)
tr$y <- as.numeric(tr$y)
tr$trait <- rep(c('ECE','LNI','OCD','PFR10YR','PFR5YR','SVI'), #'OS10YR','OS15YR',
each=nrow(tr)/6)
tr$trait <- factor(tr$trait, levels=c('PFR5YR','PFR10YR',#'OS10YR','OS15YR',
'OCD','ECE','LNI','SVI'))
points <- tr[tr$x %in% c(12,18,31),]
points
points$Panel=factor(rep(c('Oncotype','Decipher','Prolaris'), 6),
levels=c('Oncotype','Decipher','Prolaris'))
#points$Panel=factor(rep(c('Top12','Top18','Top31'), 6),
# levels=c('Top12','Top18','Top31'))
rand$y <- as.numeric(rand$y)
rand$trait <- rep(c('ECE','LNI','OCD','PFR10YR','PFR5YR','SVI'),#'OS10YR','OS15YR',
each=nrow(rand)/6)
rand$trait <- factor(rand$trait, levels=c('PFR5YR','PFR10YR',#'OS10YR','OS15YR',
'OCD','ECE','LNI','SVI'))
rand$Panel=factor(rep(c('Oncotype','Decipher','Prolaris'), 6),
levels=c('Oncotype','Decipher','Prolaris'))
#rand$Panel=factor(rep(c('Rand12','Rand18','Rand31'), 6),
# levels=c('Rand12','Rand18','Rand31'))
com$y <- as.numeric(com$y)
com$trait <- rep(c('ECE','LNI','OCD','PFR10YR','PFR5YR','SVI'),#'OS10YR','OS15YR',
each=nrow(com)/6)
com$trait <- factor(com$trait, levels=c('PFR5YR','PFR10YR',#'OS10YR','OS15YR',
'OCD','ECE','LNI','SVI'))
com$Panel=factor(rep(c('Oncotype','Decipher','Prolaris'), 6),
levels=c('Oncotype','Decipher','Prolaris'))
ggplot() + geom_line(data=tr, aes(x=x, y=y), size=0.8) +
facet_wrap(~trait, ncol=3) +
geom_hline(data=rand, aes(yintercept = y, color=Panel),linetype=1, size=1) +
geom_hline(data=com, aes(yintercept = y, color=Panel),linetype=2, size=1) +
xlab('Number of genes') + ylab('Predictability') + ylim(0,0.4) +
geom_point(data=points, aes(x=x, y=y, color=Panel), size=5, shape=20) +
theme_bw() + theme(axis.line = element_line(colour = "black"),
axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16),
axis.text.x = element_text(size=12, color='black'), #, angle=45, hjust = 1.2, vjust=0.8
axis.text.y = element_text(size=12, color='black'),
#axis.ticks.x = element_blank(),
legend.title = element_text(size=0),
legend.text = element_text(size=14),
strip.text = element_text(size=16, color='black'),
#strip.background = element_rect(fill="white", color='black'),
strip.background = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(color='black')) #strip.background = element_blank()
### No. of genes for each peak
tr %>% dplyr::group_by(trait) %>%
dplyr::summarise(x[which(y==max(y))])
### miRNA
files <-file.path('report/HAT_Nomogram', dir('report/HAT_Nomogram/')) ## NOT THESE
files
idx <- grep(pattern = 'mir', x = files, fixed = T)
idx
files <- files[idx]
files
#com <- NULL
tr <- NULL
#rand <- NULL
for (fl in files) {
hat <- read.delim(fl, stringsAsFactors = F)
#rand <- rbind(rand, hat[1:3,])
#com <- rbind(com, hat[4:6,])
tr <- rbind(tr, hat[4:nrow(hat),])
}
tr$x <- as.numeric(tr$x)
tr$y <- as.numeric(tr$y)
tr$trait <- rep(c('ECE','LNI','OCD','PFR10YR','PFR5YR','SVI'), #'OS10YR','OS15YR',
each=nrow(tr)/6)
tr$trait <- factor(tr$trait, levels=c('PFR5YR','PFR10YR',#'OS10YR','OS15YR',
'OCD','ECE','LNI','SVI'))
ggplot() + geom_line(data=tr, aes(x=x, y=y), size=0.8) +
facet_wrap(~trait, ncol=3) +
#geom_hline(data=rand, aes(yintercept = y, color=Panel),linetype=2, size=1) +
#geom_hline(data=com, aes(yintercept = y, color=Panel),linetype=1, size=1) +
xlab('Number of miRNAs') + ylab('Predictability') + ylim(0,0.42) +
#geom_point(data=points, aes(x=x, y=y, color=Panel), size=5, shape=20) +
theme_bw() + theme(axis.line = element_line(colour = "black"),
axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16),
axis.text.x = element_text(size=12, color='black'), #, angle=45, hjust = 1.2, vjust=0.8
axis.text.y = element_text(size=12, color='black'),
#axis.ticks.x = element_blank(),
legend.title = element_text(size=0),
legend.text = element_text(size=14),
strip.text = element_text(size=16, color='black'),
#strip.background = element_rect(fill="white", color='black'),
strip.background = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(color='black')) #strip.background = element_blank()
### No. of genes for each peak
tr %>% dplyr::group_by(trait) %>%
dplyr::summarise(x[which(y==max(y))])
##############################################################
############# Figure 4: Integration of gene and miRNA panels
hat <- read.table('report/HAT_Nomogram/HAT_mRNA_miRNA_Peak_Improvement.txt', sep='\t', header = T, stringsAsFactors = F)
hat$Omics[hat$Omics=='TR'] <- 'Tr'
hat$Omics[hat$Omics=='MI'] <- 'Mi'
hat$Omics[hat$Omics=='TR+MI'] <- 'Tr+Mi'
hat$Omics <- factor(hat$Omics, levels=c('Tr','Mi','Tr+Mi'))
hat$Traits <- factor(hat$Traits, levels=c('PFR5YR','PFR10YR','OCD','ECE','LNI','SVI'))
### boxplot
ggplot(data=hat, aes(x=Omics, y=Predictability, color=Omics)) +
stat_boxplot(geom ='errorbar',width=0.3) +
geom_boxplot() + ylim(0.25,0.45) +
stat_summary(fun.y='mean', geom="point", shape=23, size=3) +
#geom_jitter(width = 0.1) +
theme_bw()+theme(axis.line = element_line(colour = "black"),
#panel.grid.minor = element_blank(),
axis.text = element_text(size=12, color='black'),
axis.title.y = element_text(size=14),
axis.title.x = element_blank(),
legend.text = element_blank(),
legend.position = 'none',
panel.background = element_blank(),
panel.border = element_rect(color='black'))
##### barplot
ggplot(data=hat, aes(x=Omics, y=Predictability, color=Omics, fill=Omics)) +
geom_bar(stat='identity') + ylim(0,0.45) +
facet_wrap(~Traits, ncol=6, strip.position = 'top') +
theme_bw() + theme(axis.line = element_line(colour = "black"),
axis.title.y = element_text(size=16, family='aril'),
axis.title.x = element_blank(),
#axis.text.x = element_blank(),
#axis.text.y = element_text(size=12, color = 'black'),
axis.text = element_text(size=12, colour = 'black', family = 'Aril'),
axis.ticks.x = element_blank(),
legend.position = 'none',
legend.title = element_text(size=0),
legend.text = element_text(size=12),
strip.text = element_text(size=12, face='bold', family = 'Aril'),
#strip.background = element_rect(fill="white", color='black'),
strip.background = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(color='black')) #strip.background = element_blank()