R/visualization.R
In ChenMengjie/VIPER: VIPER: a variability-preserving imputation method for single cell RNA sequencing data
PlotWeights <- function(Imputed, KnownLabel = NULL){
suppressMessages(require(easyGgplot2))
suppressMessages(require(gplots))
weights.mat <- apply(Imputed$sample_weights, 2, function(x){
y <- x
y[y>0.01] <- 1
y[y<=0.01] <- 0
y
})
palette.gr.marray <- colorRampPalette(c("white", "red"))(4)
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
heatmap.2(as.matrix(weights.mat), trace = "none", col = palette.gr.marray, symbreaks = F,
labCol = NA, dendrogram = "none", ColSideColors = col.color, labRow = NA, key = F, Colv = FALSE, Rowv = FALSE)
} else {
heatmap.2(as.matrix(weights.mat), trace = "none", col = palette.gr.marray, symbreaks = F,
labCol = NA, dendrogram = "none", labRow = NA, key = F, Colv = FALSE, Rowv = FALSE)
}
}
PlotGeneMatrix <- function(Imputed, GeneExpression = NULL, KnownLabel = NULL, GeneNum = 2000){
suppressMessages(require(grid))
suppressMessages(require(gplots))
suppressMessages(require(gridExtra))
suppressMessages(require(gridGraphics))
GeneExpression_log <- apply(GeneExpression, 2, function(x){log(x+0.1)})
Imputed_log <- Imputed$imputed_log
p <- nrow(Imputed$imputed_log)
if(p > GeneNum){
#gene.var <- apply(GeneExpression_log, 1, var)
#cutoff <- sort(gene.var, decreasing = TRUE)[GeneNum]
#fll <- c(1:p)[gene.var >= cutoff]
selected <- round(runif(GeneNum)*p)
fll <- c(1:p)[selected]
} else {
fll <- c(1:p)
}
palette.gr.marray <- colorRampPalette(c("blue", "white", "red"))(56)
grab_grob <- function(){
grid.grab()
}
if(!is.null(GeneExpression)){
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
aa <- heatmap.2(as.matrix(GeneExpression_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
bb <- rev(aa$rowInd)
draw_H1 <- function(data){
heatmap.2(as.matrix(data), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = FALSE)
grab_grob()
}
kk1 <- draw_H1(GeneExpression_log[fll, ][bb, ])
kk2 <- draw_H1(Imputed_log[fll, ][bb, ])
gl <- list(kk1, kk2)
grid.newpage()
grid.arrange(grobs=gl, ncol=2, clip=TRUE)
} else {
bb <- rev(aa$rowInd)
aa <- heatmap.2(as.matrix(GeneExpression[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
draw_H1 <- function(data){
heatmap.2(as.matrix(data), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = FALSE)
grab_grob()
}
kk1 <- draw_H1(GeneExpression_log[fll, ][bb, ])
kk2 <- draw_H1(Imputed_log[fll, ][bb, ])
gl <- list(kk1, kk2)
grid.newpage()
grid.arrange(grobs=gl, ncol=2, clip=TRUE)
}
} else {
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
heatmap.2(as.matrix(Imputed_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color, labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
} else {
heatmap.2(as.matrix(Imputed_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
}
}
}
PlotCV <- function(Imputed, GeneExpression, KnownLabel = NULL, GeneNum = 2000, selection = c("Zero", "Mean")){
selection <- match.arg(selection )
suppressMessages(require(gplots))
logxx <- apply(GeneExpression, 2, function(y){log(y + 0.1)})
logxx[GeneExpression==0] <- 0
Imputed_log <- Imputed$imputed_log
CV.all <- function(data){
CV <- function(mean, sd){
abs(sd/mean)
}
CV.per.gene <- apply(data, 1, function(x){
CV(mean(x), sd(x))
})
CV.per.gene
}
CV.nonzero <- function(data){
CV <- function(mean, sd){
abs(sd/mean)
}
CV.per.gene <- apply(data, 1, function(x){
CV(mean(x[x!=0]), sd(x[x!=0]))
})
CV.per.gene
}
if(!is.null(KnownLabel)){
types <- unique(KnownLabel)
len <- length(types)
show.some <- sample(1:nrow(GeneExpression), GeneNum)
for(i in 1:len){
flag <- which(KnownLabel%in%types[i])
raw.data <- logxx[, flag]
zero.num <- apply(raw.data, 1, function(x){
length(x[x==0])
})
zero.rate <- round(zero.num/ncol(logxx), 2)*100
dropout.rate <- apply(raw.data, 1, function(x){
round(mean(x[x!=0]), 2)
})
SIMPLE.CV <- CV.all(Imputed_log[, flag])[show.some]
raw.cv <- CV.nonzero(logxx[, flag])[show.some]
zero.rate.selected <- zero.rate[show.some]
dropout.rate.selected <- dropout.rate[show.some]
df <- data.frame(value = SIMPLE.CV, compr = rep("Without-imputation", GeneNum), raw = raw.cv, zero = zero.rate.selected,
dropout = dropout.rate.selected)
if(selection=="Zero"){
gg1 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.5,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
mainTitle = types[i], removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Percentage \n of Zero", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = zero), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,
colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg1)
} else {
gg2 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.5,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
mainTitle = types[i], removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Mean of nonzero \n values", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = dropout), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg2)
}
}
} else {
show.some <- sample(1:nrow(GeneExpression), GeneNum)
raw.data <- logxx
zero.num <- apply(raw.data, 1, function(x){
length(x[x==0])
})
zero.rate <- round(zero.num/ncol(logxx), 2)*100
dropout.rate <- apply(raw.data, 1, function(x){
round(mean(x[x!=0]), 2)
})
SIMPLE.CV <- CV.all(Imputed_log)[show.some]
raw.cv <- CV.nonzero(logxx)[show.some]
zero.rate.selected <- zero.rate[show.some]
dropout.rate.selected <- dropout.rate[show.some]
df <- data.frame(value = SIMPLE.CV, compr = rep("Without-imputation", GeneNum), raw = raw.cv, zero = zero.rate.selected,
dropout = dropout.rate.selected)
if(selection=="Zero"){
gg1 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.2,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Percentage \n of Zero", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = zero), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,
colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg1)
} else {
gg2 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.2,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Mean of nonzero \n values", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = dropout), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg2)
}
}
}
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
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))
}
}
}
ChenMengjie/VIPER documentation built on June 15, 2019, 2:15 a.m.
R Package Documentation
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PlotWeights <- function(Imputed, KnownLabel = NULL){
suppressMessages(require(easyGgplot2))
suppressMessages(require(gplots))
weights.mat <- apply(Imputed$sample_weights, 2, function(x){
y <- x
y[y>0.01] <- 1
y[y<=0.01] <- 0
y
})
palette.gr.marray <- colorRampPalette(c("white", "red"))(4)
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
heatmap.2(as.matrix(weights.mat), trace = "none", col = palette.gr.marray, symbreaks = F,
labCol = NA, dendrogram = "none", ColSideColors = col.color, labRow = NA, key = F, Colv = FALSE, Rowv = FALSE)
} else {
heatmap.2(as.matrix(weights.mat), trace = "none", col = palette.gr.marray, symbreaks = F,
labCol = NA, dendrogram = "none", labRow = NA, key = F, Colv = FALSE, Rowv = FALSE)
}
}
PlotGeneMatrix <- function(Imputed, GeneExpression = NULL, KnownLabel = NULL, GeneNum = 2000){
suppressMessages(require(grid))
suppressMessages(require(gplots))
suppressMessages(require(gridExtra))
suppressMessages(require(gridGraphics))
GeneExpression_log <- apply(GeneExpression, 2, function(x){log(x+0.1)})
Imputed_log <- Imputed$imputed_log
p <- nrow(Imputed$imputed_log)
if(p > GeneNum){
#gene.var <- apply(GeneExpression_log, 1, var)
#cutoff <- sort(gene.var, decreasing = TRUE)[GeneNum]
#fll <- c(1:p)[gene.var >= cutoff]
selected <- round(runif(GeneNum)*p)
fll <- c(1:p)[selected]
} else {
fll <- c(1:p)
}
palette.gr.marray <- colorRampPalette(c("blue", "white", "red"))(56)
grab_grob <- function(){
grid.grab()
}
if(!is.null(GeneExpression)){
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
aa <- heatmap.2(as.matrix(GeneExpression_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
bb <- rev(aa$rowInd)
draw_H1 <- function(data){
heatmap.2(as.matrix(data), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = FALSE)
grab_grob()
}
kk1 <- draw_H1(GeneExpression_log[fll, ][bb, ])
kk2 <- draw_H1(Imputed_log[fll, ][bb, ])
gl <- list(kk1, kk2)
grid.newpage()
grid.arrange(grobs=gl, ncol=2, clip=TRUE)
} else {
bb <- rev(aa$rowInd)
aa <- heatmap.2(as.matrix(GeneExpression[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
draw_H1 <- function(data){
heatmap.2(as.matrix(data), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color,
labRow = NA, key = T, Colv = FALSE, Rowv = FALSE)
grab_grob()
}
kk1 <- draw_H1(GeneExpression_log[fll, ][bb, ])
kk2 <- draw_H1(Imputed_log[fll, ][bb, ])
gl <- list(kk1, kk2)
grid.newpage()
grid.arrange(grobs=gl, ncol=2, clip=TRUE)
}
} else {
if(!is.null(KnownLabel)){
colorlist <- c("turquoise4", "cyan", "lavender", "gold", "slateblue1", "violet", "skyblue1", "khaki", "pink", "salmon", "limegreen", "chocolate", "maroon", "purple", "blue", "yellow", "red", "brown", '#FFAAD4', '#00CC00', '#66B2FF', '#B266FF', '#FFD4AA', '#AAFFD4', '#CC0000', "#B266CC")
col.color <- colorlist[as.numeric(as.factor(KnownLabel))]
heatmap.2(as.matrix(Imputed_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", ColSideColors = col.color, labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
} else {
heatmap.2(as.matrix(Imputed_log[fll, ]), trace = "none", col = palette.gr.marray,
symbreaks = T, labCol = NA, dendrogram = "none", labRow = NA, key = T, Colv = FALSE, Rowv = TRUE)
}
}
}
PlotCV <- function(Imputed, GeneExpression, KnownLabel = NULL, GeneNum = 2000, selection = c("Zero", "Mean")){
selection <- match.arg(selection )
suppressMessages(require(gplots))
logxx <- apply(GeneExpression, 2, function(y){log(y + 0.1)})
logxx[GeneExpression==0] <- 0
Imputed_log <- Imputed$imputed_log
CV.all <- function(data){
CV <- function(mean, sd){
abs(sd/mean)
}
CV.per.gene <- apply(data, 1, function(x){
CV(mean(x), sd(x))
})
CV.per.gene
}
CV.nonzero <- function(data){
CV <- function(mean, sd){
abs(sd/mean)
}
CV.per.gene <- apply(data, 1, function(x){
CV(mean(x[x!=0]), sd(x[x!=0]))
})
CV.per.gene
}
if(!is.null(KnownLabel)){
types <- unique(KnownLabel)
len <- length(types)
show.some <- sample(1:nrow(GeneExpression), GeneNum)
for(i in 1:len){
flag <- which(KnownLabel%in%types[i])
raw.data <- logxx[, flag]
zero.num <- apply(raw.data, 1, function(x){
length(x[x==0])
})
zero.rate <- round(zero.num/ncol(logxx), 2)*100
dropout.rate <- apply(raw.data, 1, function(x){
round(mean(x[x!=0]), 2)
})
SIMPLE.CV <- CV.all(Imputed_log[, flag])[show.some]
raw.cv <- CV.nonzero(logxx[, flag])[show.some]
zero.rate.selected <- zero.rate[show.some]
dropout.rate.selected <- dropout.rate[show.some]
df <- data.frame(value = SIMPLE.CV, compr = rep("Without-imputation", GeneNum), raw = raw.cv, zero = zero.rate.selected,
dropout = dropout.rate.selected)
if(selection=="Zero"){
gg1 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.5,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
mainTitle = types[i], removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Percentage \n of Zero", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = zero), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,
colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg1)
} else {
gg2 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.5,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
mainTitle = types[i], removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Mean of nonzero \n values", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = dropout), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg2)
}
}
} else {
show.some <- sample(1:nrow(GeneExpression), GeneNum)
raw.data <- logxx
zero.num <- apply(raw.data, 1, function(x){
length(x[x==0])
})
zero.rate <- round(zero.num/ncol(logxx), 2)*100
dropout.rate <- apply(raw.data, 1, function(x){
round(mean(x[x!=0]), 2)
})
SIMPLE.CV <- CV.all(Imputed_log)[show.some]
raw.cv <- CV.nonzero(logxx)[show.some]
zero.rate.selected <- zero.rate[show.some]
dropout.rate.selected <- dropout.rate[show.some]
df <- data.frame(value = SIMPLE.CV, compr = rep("Without-imputation", GeneNum), raw = raw.cv, zero = zero.rate.selected,
dropout = dropout.rate.selected)
if(selection=="Zero"){
gg1 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.2,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Percentage \n of Zero", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = zero), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,
colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg1)
} else {
gg2 <- ggplot2.scatterplot(data = df, xName = 'raw', yName = 'value', size = 0.2,
backgroundColor = "white", xtitle="CV (Before Imputation)", ytitle="CV (After Imputation)",
removePanelGrid=TRUE, removePanelBorder=FALSE, showLegend=TRUE,
legendTitle = "Mean of nonzero \n values", legendTitleFont = c(15, "bold", "black"),
legendTextFont = c(15, "bold", "black"), mainTitleFont = c(10, "bold", "black"),
xtitleFont = c(15, "bold", "black"), ytitleFont = c(15, "bold", "black"),
xTickLabelFont = c(15, "bold", "white"), yTickLabelFont = c(15, "bold", "black")) + geom_point(aes(colour = dropout), size = 0.2) + ylim(0, 5) + xlim(0, 5) + theme(strip.text.x = element_text(size = 15,colour = "black", face = "bold")) + geom_abline(col = "brown", linetype = "dashed", size=1)
print(gg2)
}
}
}
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
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))
}
}
}
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