fig a

library(ggplot2)
library(hrbrthemes)
library(corrplot)

data <- read.csv("fig2abc_logRPM_batch1.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
matrix=cor(data[2:13])

jpeg(file="fig_a.jpg", width=5, height=5, units="in", res=800)
corrplot(corr=matrix,method = "color",tl.col="black",addCoef.col = "white",number.cex = 0.8,order = "AOE",tl.cex=1.3,cl.pos="n")
dev.off()

fig b

library(ggplot2)
library(hrbrthemes)
library(corrplot)

data <- read.csv("fig2abc_logRPM_batch1.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
matrix=cor(data[14:19])

jpeg(file="fig_b.jpg", width=5, height=5, units="in", res=800)
corrplot(corr=matrix,method = "color",tl.col="black",number.cex = 1.6,addCoef.col = "white",order = "AOE",tl.cex=1.3,cl.pos="n")
dev.off()

fig c

library(ggplot2)
library(hrbrthemes)
library(corrplot)

data <- read.csv("fig2abc_logRPM_batch1.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
matrix=cor(data[20:25])

jpeg(file="fig_c.jpg", width=5, height=5, units="in", res=800)
corrplot(corr=matrix,method = "color",tl.col="black",addCoef.col = "white",order = "AOE",number.cex = 1.4,tl.cex=1.3,cl.pos="n")
dev.off()

fig d

library(ggplot2)

data <- read.csv("fig1b-1-intron_ratio.csv", header = TRUE,sep=',',encoding="utf-8")
names(data)=c("x","y")
data$x=c("intron0_1","intron0_2","intron0_3","intron2_1","intron2_2","intron2_3","intron6_1","intron6_2","intron6_3","intron12_1","intron12_2","intron12_3",
	"total0_1","total0_2","total0_3","total12_1","total12_2","total12_3",
	"mRNA0_1","mRNA0_2","mRNA0_3","mRNA12_1","mRNA12_2","mRNA12_3",
	"batch1_mock1","batch1_mock2","batch1_mock3","batch1__intron24_1","batch1__intron24_2","batch1__intron24_3","batch1_lnc0","batch1_lnc24","batch1_mRNA0","batch1_mRNA24")
data$group=c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4)
data$group=as.character(data$group)
data1=data[1:24,]
colors1<-c("#EE6666","#FFA500","#5470c6")
plot=ggplot(data1, aes(factor(x, level=x), y,group=1,colour=group,fill=group)) +
	scale_fill_manual(values = colors1)+
	scale_color_manual(values = colors1)+
	geom_line(size = 1.5,colour ="#6495ED",lty=5) +
	geom_point(shape = 24,  size =5) +
	theme_bw() + 
	ylab("Percent of reads in the Intron regions") + 
	xlab("Sample name") + 
	theme(panel.border=element_blank(),
		axis.line=element_line(size=0.5,colour="black"),
		legend.position="n",
		axis.title.y= element_text(vjust = 2,hjust = 1),
		axis.text.y=element_text(size=18),
		axis.title=element_text(size=18),
		axis.text.x = element_text(size=18,vjust = 1, hjust = 1,angle = 65),
		panel.grid.minor.x=element_blank(),
		panel.grid.minor.y=element_blank())

jpeg(file="fig_d.jpg", width=10, height=5, units="in", res=800)
plot
dev.off()

fig e

library(ggplot2)
library(dplyr)
library(forcats)

data <- read.csv("section2_coverage_20220110.csv", header = TRUE,sep=',',encoding="utf-8")
colors<-c("#EE6666","#EE6666","#EE6666","#DA70D5","#DA70D5","#DA70D5","#fac858","#fac858","#fac858","#73c0de","#73c0de","#73c0de","#FF1493","#FF1493","#FF1493","#fc8452","#fc8452","#fc8452","#FF0000","#FF0000","#FF0000","#1E90FF","#1E90FF","#1E90FF")
plot=data %>%
	mutate(class = fct_reorder(sampleID, coverageDepth)) %>%
	ggplot( aes(x=factor(sampleID, level=c("intron0_1","intron0_2","intron0_3","intron2_1","intron2_2","intron2_3","intron6_1","intron6_2","intron6_3","intron12_1","intron12_2","intron12_3","lnc0_1","lnc0_2","lnc0_3","lnc12_1","lnc12_2","lnc12_3","mRNA0_1","mRNA0_2","mRNA0_3","mRNA12_1","mRNA12_2","mRNA12_3")), y=coverageDepth, fill=sampleID)) + 
	geom_boxplot() + 
	scale_fill_manual(values = colors)+
	coord_cartesian(ylim = c(0,30000))+
	scale_y_continuous(labels = scales::comma)+
	theme_bw() +
	scale_x_discrete(labels= c("intron0_1","intron0_2","intron0_3","intron2_1","intron2_2","intron2_3","intron6_1","intron6_2","intron6_3","intron12_1","intron12_2","intron12_3","total0_1","total0_2","total0_3","total12_1","total12_2","total12_3","mRNA0_1","mRNA0_2","mRNA0_3","mRNA12_1","mRNA12_2","mRNA12_3"))+
	theme(legend.position="none")+
	ylab("CoverageDepth") +
	xlab("SampleID") +
	theme(legend.margin=margin(0,0,0,3),
		axis.title.y = element_text(vjust = 2),
		axis.text.y=element_text(size=18),
		axis.title=element_text(size=18),
		axis.text.x = element_text(size=18,vjust = 1, hjust = 1, angle = 60),
		panel.grid.minor.x=element_blank(),
		panel.grid.minor.y=element_blank())

jpeg(file="fig_e.jpg", width=12, height=5, units="in", res=500)
plot
dev.off()

fig f

library(readxl)
library(ggplot2)
library(tidyr)

raw <- read.csv("fig2_sisVenn.csv", header = TRUE,sep=',',encoding="utf-8")
raw=raw[,3:26]
df = data.frame(x=names(raw))

vec=c()
for (i in names(raw))
{
	vec=c(vec,nrow(raw[which(raw[i]>10&raw[i]<=50),]))
}
df['>10']=vec

vec=c()
for (i in names(raw))
{
	vec=c(vec,nrow(raw[which(raw[i]>50&raw[i]<=100),]))
}
df['>50']=vec

vec=c()
for (i in names(raw))
{
	vec=c(vec,nrow(raw[which(raw[i]>100&raw[i]<=1000),]))
}
df['>100']=vec

vec=c()
for (i in names(raw))
{
	vec=c(vec,nrow(raw[which(raw[i]>100&raw[i]<=1000),]))
}
df['>1000']=vec

test1 <- gather(df, E1, E2, -x) 
test1$E1 = factor(test1$E1 , levels=c('>1000','>100','>50','>10'))
test1$x = factor(test1$x , levels=c("intron0_1","intron0_2","intron0_3","intron2_1","intron2_2","intron2_3","intron6_1","intron6_2","intron6_3","intron12_1","intron12_2","intron12_3","lnc0_1","lnc0_2","lnc0_3","lnc12_1","lnc12_2","lnc12_3","mRNA0_1","mRNA0_2","mRNA0_3","mRNA12_1","mRNA12_2","mRNA12_3"))
p=ggplot(test1,aes(x = x, y = E2, fill = E1)) +
	geom_bar(stat = "identity",position="stack") +
	scale_fill_manual(values = c("#DA70D5","#73c0de","#FAC858","#EE6666"))+
	labs( x = NULL, y = NULL,fill = "number")+
	theme_bw() + 
	xlab("Sample name")+
	ylab("Number")+
	scale_x_discrete(labels= c("intron0_1","intron0_2","intron0_3","intron2_1","intron2_2","intron2_3","intron6_1","intron6_2","intron6_3","intron12_1","intron12_2","intron12_3","total0_1","total0_2","total0_3","total12_1","total12_2","total12_3","mRNA0_1","mRNA0_2","mRNA0_3","mRNA12_1","mRNA12_2","mRNA12_3"))+
	theme(panel.grid=element_blank(),panel.border=element_blank(),axis.line=element_line(size=0.5,colour="black")) + 
	theme(axis.title=element_text(size=18),axis.text.y=element_text(size=18),axis.text.x = element_text(size=18,vjust = 1, hjust = 1, angle = 65),panel.grid.minor.x=element_blank(),panel.grid.minor.y=element_blank())+
	theme(legend.text=element_text(size=18),legend.title=element_text(size=18) )

jpeg(file="fig_f.jpg", width=10, height=5, units="in", res=500)
p
dev.off()

fig g

library(VennDiagram)

#fig1
data <- read.csv("fig2a_0h_venn.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
cap=data.frame(data$ID[data$cap>0])
names(cap)<-c("cap")
lnc=data.frame(data$ID[data$lnc>0])
names(lnc)<-c("lnc")
mRNA=data.frame(data$ID[data$mRNA>0])
names(mRNA)<-c("mRNA")
venn.plot1 =venn.diagram(
	x = list(cap$cap,lnc$lnc,mRNA$mRNA),
	filename=NULL,
	alpha=c(0.5,0.5,0.7),
	cex=2.2,
	cat.cex = 2.2,
	fontface = "bold",
	cat.fontface = "bold",
	fontfamily = "Arial",
	cat.fontfamily = "Arial",
	fill=c( "red", "blue", "orange"),
	cat.pos = c(-5,5,5),
	cat.dist = c(0.09,0.08,0.03),
	margin = 0.01,
	category = c("capture", "total", "mRNA"))

jpeg(file="fig_g1.jpg", width=5, height=5, units="in", res=500)
grid.draw(venn.plot1)
dev.off()

#fig2
data <- read.csv("fig2a_12h_venn.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
cap=data.frame(data$ID[data$cap>0])
names(cap)<-c("cap")
lnc=data.frame(data$ID[data$lnc>0])
names(lnc)<-c("lnc")
mRNA=data.frame(data$ID[data$mRNA>0])
names(mRNA)<-c("mRNA")
venn.plot1 =venn.diagram(
    x = list(cap$cap,lnc$lnc,mRNA$mRNA),
    filename=NULL,
    alpha=c(0.5,0.5,0.7),
    cex=2.2,
    cat.cex = 2.2,
    fontface = "bold",
    cat.fontface = "bold",
    fontfamily = "Arial",
    cat.fontfamily = "Arial",
    fill=c( "red", "blue", "orange"),
    cat.pos = c(0,0,0),
    cat.dist = c(0.08,0.08,0.03),
    margin = 0.01,
    category = c("capture", "total", "mRNA"))

jpeg(file="fig_g2.jpg", width=5, height=5, units="in", res=500)
grid.draw(venn.plot1)
dev.off()

fig h

library(ggplot2)

data <- read.csv("fig2c_2.csv", header = TRUE,sep=',',encoding="utf-8")
names(data)<-c("x","y")
data$x= factor(data$x , levels=data$x)
data$group=c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4)
data$group=as.character(data$group)
data1=data[1:24,]
colors1<-c("#EE6666","#FFA500","#5470c6")
plot=ggplot(data1, aes(x,  y,group=1,colour=group,fill=group)) +
	scale_fill_manual(values = colors1)+
	scale_color_manual(values = colors1)+
	geom_line(size = 1.5,colour ="#6495ED",lty=5) +
	geom_point(shape = 24, size =5) +
	theme_bw() + 
	theme(legend.position="none")+
	ylab("Number of splicing junctions") + xlab("Sample name") + 
	theme(panel.border=element_blank(),
		axis.line=element_line(size=0.5,colour="black"),
		axis.text.y=element_text(size=18),
		axis.title=element_text(size=18),
		axis.text.x = element_text(size=18,vjust = 1, hjust = 1, angle = 65),
		panel.grid.minor.x=element_blank(),
		panel.grid.minor.y=element_blank())

jpeg(file="fig_h.jpg", width=10, height=5, units="in", res=800)
plot
dev.off()

fig i

library(VennDiagram)

data <- read.csv("fig2_junction_venn.csv", header = TRUE,sep=',',encoding="utf-8",stringsAsFactors=F)
intronCapture=data.frame(data$x[data$intronCapture>0])
names(intronCapture)<-c("intronCapture")
lnc=data.frame(data$x[data$lnc>0])
names(lnc)<-c("lnc")
mRNA=data.frame(data$x[data$mRNA>0])
names(mRNA)<-c("mRNA")
venn.plot1 =venn.diagram(
    x = list(intronCapture$intronCapture,lnc$lnc,mRNA$mRNA),
    filename=NULL,
    alpha=c(0.5,0.7,0.7),
    cex=2.2,
    cat.cex = 2.2,
    fontface = "bold",
    cat.fontface = "bold",
    fontfamily = "Arial",
    cat.fontfamily = "Arial",
    fill=c( "red", "#4169E1", "yellow"),
    cat.pos = c(-55,0,40),
    cat.dist = c(0.06,-0.07,-0.1),
    margin = 0.01,
    category = c("intronCapture", "total", "mRNA"))

jpeg(file="fig_i.jpg", width=5, height=5, units="in", res=500)
grid.draw(venn.plot1)
dev.off()