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Introduction

Individual HP=19026-01

library(ggplot2)
library(ggpubr)
Loading required package: magrittr
### quantify A
A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190420_T1D_A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for( i in 1:(nrow(A_quant)-1) ){ A_quant$group[i] <- (A_quant$Sample.Type[i]!=A_quant$Sample.Type[i+1] ) }

num_SampleGroup <- sum( A_quant$group)-1

Samples <- data.frame(start= (which(A_quant$group )+1)[1:num_SampleGroup], end = which(A_quant$group)[-c(1)])[seq(1,num_SampleGroup,2),]

result_A <- result_m6A <- result_G <- NULL
for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  A_quant_std <- dplyr::mutate_if(A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  A_quant_tmp <-data.frame(Area = A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, A_quant_std)
  result_A <- rbind(result_A,cbind( "Name"= A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "A"=  predict(fit, newdata = A_quant_tmp) ) )
}

### quantify G
G_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190420_T1D_G.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  G_quant_std <- dplyr::mutate_if(G_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  G_quant_tmp <-data.frame(Area = G_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, G_quant_std)
  result_G <- rbind(result_G,cbind( "Name"= G_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "G"=  predict(fit, newdata = G_quant_tmp) ) )
}

### quantify m6A
m6A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190420_T1D_m6A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m6A_quant_std <- dplyr::mutate_if(m6A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m6A_quant_tmp <-data.frame(Area = m6A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m6A_quant_std)
  result_m6A <- rbind(result_m6A,cbind( "Name"= m6A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m6A"=  predict(fit, newdata = m6A_quant_tmp) ) )
}


result_batch1 <- data.frame( sampleName = rep( c("Basal", "Glucose 19mM","Insulin 10mM", "Palmitate 500uM","IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg"), 3) , A = as.numeric(result_A[,2]), G = as.numeric(result_G[,2]), m6A = as.numeric(result_m6A[,2]) ) 
result_batch1$m6AvsA <- result_batch1$m6A/result_batch1$A
result_batch1$m6AvsG <- result_batch1$m6A/result_batch1$G

DT::datatable(result_batch1, options = list(scrollX = TRUE, keys = TRUE, pageLength = 10),rownames = F)
ggplot(result_batch1,aes( x = sampleName, y = m6AvsA))+geom_boxplot( )+ xlab("Samples") +ylab("m6A/A")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))

Version Author Date
6c0f6d5 scottzijiezhang 2019-05-16 
ggplot(result_batch1)+geom_boxplot(aes( x = sampleName, y = m6AvsG) )+ xlab("Samples") +ylab("m6A/G") +theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))

Version Author Date
6c0f6d5 scottzijiezhang 2019-05-16

            ### Individual HP=19030-01

library(ggplot2)
library(ggpubr)

### quantify A
A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190515_T1D_A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for( i in 1:(nrow(A_quant)-1) ){ A_quant$group[i] <- (A_quant$Sample.Type[i]!=A_quant$Sample.Type[i+1] ) }

num_SampleGroup <- sum( A_quant$group)-1

Samples <- data.frame(start= (which(A_quant$group )+1)[1:num_SampleGroup], end = which(A_quant$group)[-c(1)])[seq(1,num_SampleGroup,2),]

result_A <- result_m6A <- result_G <- result_m1A <- NULL
for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  A_quant_std <- dplyr::mutate_if(A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  A_quant_tmp <-data.frame(Area = A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, A_quant_std)
  result_A <- rbind(result_A,cbind( "Name"= A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "A"=  predict(fit, newdata = A_quant_tmp) ) )
}

### quantify G
G_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190515_T1D_G.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  G_quant_std <- dplyr::mutate_if(G_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  G_quant_tmp <-data.frame(Area = G_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, G_quant_std)
  result_G <- rbind(result_G,cbind( "Name"= G_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "G"=  predict(fit, newdata = G_quant_tmp) ) )
}

### quantify m6A
m6A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190515_T1D_m6A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m6A_quant_std <- dplyr::mutate_if(m6A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m6A_quant_tmp <-data.frame(Area = m6A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m6A_quant_std)
  result_m6A <- rbind(result_m6A,cbind( "Name"= m6A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m6A"=  predict(fit, newdata = m6A_quant_tmp) ) )
}

### quantify m1A
m1A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190515_T1D_m1A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m1A_quant_std <- dplyr::mutate_if(m1A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m1A_quant_tmp <-data.frame(Area = m1A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m1A_quant_std)
  result_m1A <- rbind(result_m1A,cbind( "Name"= m1A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m1A"=  predict(fit, newdata = m1A_quant_tmp) ) )
}


result_batch2 <- data.frame( sampleName = rep( c("Basal", "Glucose 19mM","Insulin 10mM", "Palmitate 500uM","IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg"), 3) , A = as.numeric(result_A[,2]), G = as.numeric(result_G[,2]), m6A = as.numeric(result_m6A[,2]), m1A =  as.numeric(result_m1A[,2]) ) 
result_batch2$m6AvsA <- result_batch2$m6A/result_batch2$A
result_batch2$m6AvsG <- result_batch2$m6A/result_batch2$G
result_batch2$m1AvsA <- result_batch2$m1A/result_batch2$A
result_batch2$m1AvsG <- result_batch2$m1A/result_batch2$G

DT::datatable(result_batch2, options = list(scrollX = TRUE, keys = TRUE, pageLength = 10),rownames = F)
result_batch2_m6A_melt <- data.frame(sampleName = rep(result_batch2$sampleName,2), m6A = c(result_batch2$m6AvsA,result_batch2$m6AvsG), compareTo = c(rep("A",nrow(result_batch2) ),rep("G",nrow(result_batch2))) )
ggplot(result_batch2_m6A_melt,aes( x = sampleName, y = m6A, color = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m6A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch2$m6AvsA[result_batch2$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch2$m6AvsG[result_batch2$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
f4697b0 scottzijiezhang 2019-05-16
6c0f6d5 scottzijiezhang 2019-05-16 
result_batch2_m1A_melt <- data.frame(sampleName = rep(result_batch2$sampleName,2), m1A = c(result_batch2$m1AvsA,result_batch2$m1AvsG), compareTo = c(rep("A",nrow(result_batch2) ),rep("G",nrow(result_batch2))) )
ggplot(result_batch2_m1A_melt,aes( x = sampleName, y = m1A, colour = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m1A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch2$m1AvsA[result_batch2$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch2$m1AvsG[result_batch2$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
f4697b0 scottzijiezhang 2019-05-16
6c0f6d5 scottzijiezhang 2019-05-16

            ### Individual HP-19038-01 and SAMN10977276

library(ggplot2)
library(ggpubr)

### quantify A
A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/T1D_20190517_A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for( i in 1:(nrow(A_quant)-1) ){ A_quant$group[i] <- (A_quant$Sample.Type[i]!=A_quant$Sample.Type[i+1] ) }

num_SampleGroup <- sum( A_quant$group)-1

Samples <- data.frame(start= (which(A_quant$group )+1)[1:num_SampleGroup], end = which(A_quant$group)[-c(1)])[seq(1,num_SampleGroup,2),]

result_A <- result_m6A <- result_G <- result_m1A <- NULL
for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  A_quant_std <- dplyr::mutate_if(A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  A_quant_tmp <-data.frame(Area = A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, A_quant_std)
  result_A <- rbind(result_A,cbind( "Name"= A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "A"=  predict(fit, newdata = A_quant_tmp) ) )
}

### quantify G
G_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/T1D_20190517_G.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  G_quant_std <- dplyr::mutate_if(G_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  G_quant_tmp <-data.frame(Area = G_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, G_quant_std)
  result_G <- rbind(result_G,cbind( "Name"= G_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "G"=  predict(fit, newdata = G_quant_tmp) ) )
}

### quantify m6A
m6A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/T1D_20190517_m6A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m6A_quant_std <- dplyr::mutate_if(m6A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m6A_quant_tmp <-data.frame(Area = m6A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m6A_quant_std)
  result_m6A <- rbind(result_m6A,cbind( "Name"= m6A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m6A"=  predict(fit, newdata = m6A_quant_tmp) ) )
}

### quantify m1A
m1A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/T1D_20190517_m1A.txt", sep = "\t", header = T, stringsAsFactors = F)[-c(1),c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m1A_quant_std <- dplyr::mutate_if(m1A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m1A_quant_tmp <-data.frame(Area = m1A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m1A_quant_std)
  result_m1A <- rbind(result_m1A,cbind( "Name"= m1A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m1A"=  predict(fit, newdata = m1A_quant_tmp) ) )
}


result_batch3 <- data.frame( sampleName = rep( c("Basal", "Glucose 19mM","Insulin 10mM", "Palmitate 500uM","IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg"), 3) , A = as.numeric(result_A[1:30,2]), G = as.numeric(result_G[1:30,2]), m6A = as.numeric(result_m6A[1:30,2]), m1A =  as.numeric(result_m1A[1:30,2]) ) 
result_batch3$m6AvsA <- result_batch3$m6A/result_batch3$A
result_batch3$m6AvsG <- result_batch3$m6A/result_batch3$G
result_batch3$m1AvsA <- result_batch3$m1A/result_batch3$A
result_batch3$m1AvsG <- result_batch3$m1A/result_batch3$G

result_batch4 <- data.frame( sampleName = rep( c("Basal", "Glucose 19mM","Insulin 10mM", "Palmitate 500uM","IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg"), 3) , A = as.numeric(result_A[31:60,2]), G = as.numeric(result_G[31:60,2]), m6A = as.numeric(result_m6A[31:60,2]), m1A =  as.numeric(result_m1A[31:60,2]) ) 
result_batch4$m6AvsA <- result_batch4$m6A/result_batch4$A
result_batch4$m6AvsG <- result_batch4$m6A/result_batch4$G
result_batch4$m1AvsA <- result_batch4$m1A/result_batch4$A
result_batch4$m1AvsG <- result_batch4$m1A/result_batch4$G

HP-19038-01

DT::datatable(result_batch3, options = list(scrollX = TRUE, keys = TRUE, pageLength = 10),rownames = F)
result_batch3_m6A_melt <- data.frame(sampleName = rep(result_batch3$sampleName,2), m6A = c(result_batch3$m6AvsA,result_batch3$m6AvsG), compareTo = c(rep("A",nrow(result_batch3) ),rep("G",nrow(result_batch3))) )
ggplot(result_batch3_m6A_melt,aes( x = sampleName, y = m6A, color = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m6A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch3$m6AvsA[result_batch3$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch3$m6AvsG[result_batch3$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
2616200 scottzijiezhang 2019-05-20 
result_batch3_m1A_melt <- data.frame(sampleName = rep(result_batch3$sampleName,2), m1A = c(result_batch3$m1AvsA,result_batch3$m1AvsG), compareTo = c(rep("A",nrow(result_batch3) ),rep("G",nrow(result_batch3))) )
ggplot(result_batch3_m1A_melt,aes( x = sampleName, y = m1A, colour = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m1A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch3$m1AvsA[result_batch3$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch3$m1AvsG[result_batch3$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
2616200 scottzijiezhang 2019-05-20

    SAMN10977276

DT::datatable(result_batch4, options = list(scrollX = TRUE, keys = TRUE, pageLength = 10),rownames = F)
result_batch4_m6A_melt <- data.frame(sampleName = rep(result_batch4$sampleName,2), m6A = c(result_batch4$m6AvsA,result_batch4$m6AvsG), compareTo = c(rep("A",nrow(result_batch4) ),rep("G",nrow(result_batch4))) )
ggplot(result_batch4_m6A_melt,aes( x = sampleName, y = m6A, color = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m6A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch4$m6AvsA[result_batch4$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch4$m6AvsG[result_batch4$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
2616200 scottzijiezhang 2019-05-20 
result_batch4_m1A_melt <- data.frame(sampleName = rep(result_batch4$sampleName,2), m1A = c(result_batch4$m1AvsA,result_batch4$m1AvsG), compareTo = c(rep("A",nrow(result_batch4) ),rep("G",nrow(result_batch4))) )
ggplot(result_batch4_m1A_melt,aes( x = sampleName, y = m1A, colour = compareTo))+geom_boxplot( )+ xlab("Samples") +ylab("m1A/(A or G)")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))+geom_abline(intercept = mean(result_batch4$m1AvsA[result_batch4$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#F8766D"  )+geom_abline(intercept = mean(result_batch4$m1AvsG[result_batch4$sampleName=="Basal"]), slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
2616200 scottzijiezhang 2019-05-20

Normalize batch1 and batch2 data to last batch of data and show all.

Since different batch of samples were run using different standard set, I normalized the basal conditon so that they are comparable. The first batch had very large standard error because the QQQ machine wasn’t very stable. The later three batches were ran after the machine was tuned and is much more reliable.

basalMean_m6A <- mean(c(result_batch3[result_batch3$sampleName =="Basal","m6AvsA"], result_batch4[result_batch3$sampleName =="Basal","m6AvsA"]))
sizeFactor_m6A <- c(mean(result_batch1[result_batch1$sampleName =="Basal","m6AvsA"]), 
                    mean(result_batch2[result_batch1$sampleName =="Basal","m6AvsA"]), 
                    mean(result_batch3[result_batch1$sampleName =="Basal","m6AvsA"]),
                    mean(result_batch4[result_batch1$sampleName =="Basal","m6AvsA"]))/basalMean_m6A

all_m6A <- data.frame( condition = rep( levels(result_batch1$sampleName), 4),
                       sample = c(rep("HP-19026-01",10),rep("HP-19030-01",10),rep("HP-19038-01",10),rep("SAMN10977276",10)),
                       m6AvsA = c( tapply(result_batch1$m6AvsA,result_batch1$sampleName,mean)/sizeFactor_m6A[1], 
                                   tapply(result_batch2$m6AvsA,result_batch2$sampleName,mean)/sizeFactor_m6A[2],
                                   tapply(result_batch3$m6AvsA,result_batch3$sampleName,mean)/sizeFactor_m6A[3],
                                   tapply(result_batch4$m6AvsA,result_batch4$sampleName,mean)/sizeFactor_m6A[4]),
                       sd = c( tapply(result_batch1$m6AvsA,result_batch1$sampleName,sd)/sizeFactor_m6A[1], 
                                   tapply(result_batch2$m6AvsA,result_batch2$sampleName,sd)/sizeFactor_m6A[2],
                                   tapply(result_batch3$m6AvsA,result_batch3$sampleName,sd)/sizeFactor_m6A[3],
                                   tapply(result_batch4$m6AvsA,result_batch4$sampleName,sd)/sizeFactor_m6A[4])
                       )


ggplot(all_m6A, aes(x = condition, y = m6AvsA*100, fill = sample))+geom_bar(stat = "identity", position=position_dodge() )+ylab("m6A/A Ratio (%)")+
  geom_errorbar(aes(ymin = (m6AvsA-sd)*100, ymax = (m6AvsA+sd)*100 ) , width = 0.5, position=position_dodge(0.9)  )+ theme_bw()  + xlab("Features")+
    geom_hline(yintercept = 0,linetype="dotted", colour = "red")+theme(axis.ticks = element_blank(), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text(face="bold",size = 18, colour = "black",hjust = 0.3, angle = -20),axis.text.y = element_text(face="bold",size =25),axis.title.x = element_blank(), axis.title.y = element_text(face="bold",size =25) )+geom_abline(intercept = mean(all_m6A$m6AvsA[all_m6A$condition == "Basal"])*100, slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
605c6d1 scottzijiezhang 2019-05-20
2616200 scottzijiezhang 2019-05-20 
basalMean_m1A <- mean(c(result_batch3[result_batch3$sampleName =="Basal","m1AvsA"], result_batch4[result_batch3$sampleName =="Basal","m1AvsA"]) )
sizeFactor_m1A <- c(mean(result_batch2[result_batch1$sampleName =="Basal","m1AvsA"]), 
                    mean(result_batch3[result_batch1$sampleName =="Basal","m1AvsA"]),
                    mean(result_batch4[result_batch1$sampleName =="Basal","m1AvsA"]))/basalMean_m1A

all_m1A <- data.frame( condition = rep( levels(result_batch1$sampleName), 3),
                       sample = c(rep("HP-19030-01",10),rep("HP-19038-01",10),rep("SAMN10977276",10)),
                       m1AvsA = c( tapply(result_batch2$m1AvsA,result_batch1$sampleName,mean)/sizeFactor_m1A[1], 
                                   tapply(result_batch3$m1AvsA,result_batch2$sampleName,mean)/sizeFactor_m1A[2],
                                   tapply(result_batch4$m1AvsA,result_batch3$sampleName,mean)/sizeFactor_m1A[3]),
                       sd = c(     tapply(result_batch2$m1AvsA,result_batch2$sampleName,sd)/sizeFactor_m6A[1],
                                   tapply(result_batch3$m1AvsA,result_batch3$sampleName,sd)/sizeFactor_m6A[2],
                                   tapply(result_batch4$m1AvsA,result_batch4$sampleName,sd)/sizeFactor_m6A[3])
                       )


ggplot(all_m1A, aes(x = condition, y = m1AvsA*100, fill = sample))+geom_bar(stat = "identity", position=position_dodge() )+ylab("m1A/A Ratio (%)")+
  geom_errorbar(aes(ymin =( m1AvsA-sd)*100, ymax = (m1AvsA+sd)*100 ) , width = 0.5, position=position_dodge(0.9)  )+ theme_bw()  + xlab("Features")+
    geom_hline(yintercept = 0,linetype="dotted", colour = "red")+theme(axis.ticks = element_blank(), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text(face="bold",size = 18, colour = "black",hjust = 0.3, angle = -20),axis.text.y = element_text(face="bold",size =25),axis.title.x = element_blank(), axis.title.y = element_text(face="bold",size =25) )+geom_abline(intercept = mean(all_m1A$m1AvsA[all_m1A$condition == "Basal"])*100, slope = 0, lty = "dashed", colour ="#00BFC4"  )

Version Author Date
605c6d1 scottzijiezhang 2019-05-20
2616200 scottzijiezhang 2019-05-20

    The earlier batch of HP=19026-1 has very large technical variation because the QQQ machine wasn’t in a good condition at that time. So I re-masured this batch later after we tuned the QQQ machine. The new batch has much smaller technical variation. ### Individual HP-19026-01

library(ggplot2)
library(ggpubr)

### quantify A
A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190521_T1D_A.txt", sep = "\t", header = T, stringsAsFactors = F)[1:50,c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for( i in 1:(nrow(A_quant)-1) ){ A_quant$group[i] <- (A_quant$Sample.Type[i]!=A_quant$Sample.Type[i+1] ) }

num_SampleGroup <- sum( A_quant$group)-1

Samples <- data.frame(start= (which(A_quant$group )+1)[1:num_SampleGroup], end = which(A_quant$group)[-c(1)])[seq(1,num_SampleGroup,2),]

result_A <- result_m6A <- result_m1A <- NULL
for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  A_quant_std <- dplyr::mutate_if(A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  A_quant_tmp <-data.frame(Area = A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, A_quant_std)
  result_A <- rbind(result_A,cbind( "Name"= A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "A"=  predict(fit, newdata = A_quant_tmp) ) )
}

### quantify m6A
m6A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190521_T1D_m6A.txt", sep = "\t", header = T, stringsAsFactors = F)[1:50,c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m6A_quant_std <- dplyr::mutate_if(m6A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m6A_quant_tmp <-data.frame(Area = m6A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m6A_quant_std)
  result_m6A <- rbind(result_m6A,cbind( "Name"= m6A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m6A"=  predict(fit, newdata = m6A_quant_tmp) ) )
}

### quantify m1A
m1A_quant <- read.table("~/Rohit_T1D/Human_Islet_Stimu_QQQ/20190521_T1D_m1A.txt", sep = "\t", header = T, stringsAsFactors = F)[1:50,c("Sample.Name","Sample.Type","Actual.Concentration","Area")]

for(i in 1:nrow(Samples) ){
  std_ID <- c( (Samples$start[i]-5):( Samples$start[i]-1), (Samples$end[i]+1):( Samples$end[i]+5))
  m1A_quant_std <- dplyr::mutate_if(m1A_quant[std_ID,c("Actual.Concentration","Area")], is.character , as.numeric ) 
  m1A_quant_tmp <-data.frame(Area = m1A_quant[Samples$start[i]:Samples$end[i],c("Area")]) 
  fit <- lm(Actual.Concentration~Area, m1A_quant_std)
  result_m1A <- rbind(result_m1A,cbind( "Name"= m1A_quant$Sample.Name[Samples$start[i]:Samples$end[i]], "m1A"=  predict(fit, newdata = m1A_quant_tmp) ) )
}


result_batch1 <- data.frame( sampleName = rep( c("Basal", "Glucose 19mM","Insulin 10mM", "Palmitate 500uM","IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg"), 3) , A = as.numeric(result_A[1:30,2]), m1A = as.numeric(result_m1A[1:30,2]), m6A = as.numeric(result_m6A[1:30,2]) ) 
result_batch1$m6AvsA <- result_batch1$m6A/result_batch1$A
result_batch1$m1AvsA <- result_batch1$m1A/result_batch1$A


DT::datatable(result_batch1, options = list(scrollX = TRUE, keys = TRUE, pageLength = 10),rownames = F)
ggplot(result_batch1,aes( x = sampleName, y = m6AvsA))+geom_boxplot( )+ xlab("Samples") +ylab("m6A/A")+theme_bw()+theme(text = element_text(face = "bold", size = 20, color = "black"),axis.text = element_text(face = "bold", size = 16, color = "black"),axis.text.x = element_text(angle = 30,vjust = 0.7), axis.line = element_line(colour = "black", size = 0.75))
Warning: Removed 1 rows containing non-finite values (stat_boxplot).

Version Author Date
ce13e24 scottzijiezhang 2020-08-13

New sumarized data with updated HP-19026-01 measurement.

library(ggsci)

## calculate a size factor to correct the variation of overal measurement in batch2 sample due to standard set used in that batch.
basalMean_m6A <- mean(c(result_batch1[result_batch3$sampleName =="Basal","m6AvsA"],result_batch3[result_batch3$sampleName =="Basal","m6AvsA"], result_batch4[result_batch3$sampleName =="Basal","m6AvsA"]))
sizeFactor_m6A <-  mean(result_batch2[result_batch1$sampleName =="Basal","m6AvsA"])/basalMean_m6A

all_m6A_new <- data.frame( condition = rep( levels(result_batch1$sampleName), 4),
                       sample = c(rep("HP-19026-01",10),rep("HP-19030-01",10),rep("HP-19038-01",10),rep("SAMN10977276",10)),
                       m6AvsA = c( tapply(result_batch1$m6AvsA,result_batch1$sampleName,mean, na.rm = T), 
                                   tapply(result_batch2$m6AvsA,result_batch2$sampleName,mean)/sizeFactor_m6A,
                                   tapply(result_batch3$m6AvsA,result_batch3$sampleName,mean),
                                   tapply(result_batch4$m6AvsA,result_batch4$sampleName,mean) ),
                       sd = c( tapply(result_batch1$m6AvsA,result_batch1$sampleName,sd, na.rm = T), 
                                   tapply(result_batch2$m6AvsA,result_batch2$sampleName,sd)/sizeFactor_m6A,
                                   tapply(result_batch3$m6AvsA,result_batch3$sampleName,sd),
                                   tapply(result_batch4$m6AvsA,result_batch4$sampleName,sd))
                       )

ggplot(all_m6A_new, aes(x = condition, y = m6AvsA*100, fill = sample))+geom_bar(stat = "identity", position=position_dodge() )+ylab("m6A/A Ratio (%)")+
  geom_errorbar(aes(ymin = (m6AvsA-sd)*100, ymax = (m6AvsA+sd)*100 ) , width = 0.5, position=position_dodge(0.9)  )+ theme_bw()  + xlab("Features")+
    geom_hline(yintercept = 0,linetype="dotted", colour = "red")+theme(axis.ticks = element_blank(), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text(face="bold",size = 18, colour = "black",hjust = 0.3, angle = -20),axis.text.y = element_text(face="bold",size =25, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text(face="bold",size =25) )+geom_abline(intercept = mean(all_m6A$m6AvsA[all_m6A$condition == "Basal"])*100, slope = 0, lty = "dashed", colour ="#00BFC4"  )+scale_fill_jco()

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
cbbPalette <- c( "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

m6A_toBasal <- dplyr::filter(all_m6A_new, condition %in% c("IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg") )
m6A_toBasal$m6AvsA <- m6A_toBasal$m6AvsA/rep( dplyr::filter(all_m6A_new, condition =="Basal" )$m6AvsA, c(6,6,6,6) )

m6A_toBasal$condition <- factor(m6A_toBasal$condition, levels = c("IL1b","INFa","INFg","TNFa","IL1b+INFa","IL1b+INFg") )

ggplot( m6A_toBasal, aes( x= condition, y = m6AvsA*100 , fill = condition) )+geom_boxplot(size = 0.9)+ylab("m6A/A Ratio \n(% to Basal condition)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 14, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =14, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =14), legend.position = "none" , panel.grid = element_blank())+geom_abline(intercept = mean(all_m6A$m6AvsA[all_m6A$condition == "Basal"])*100, slope = 0, lty = "dashed", colour ="#00BFC4"  )+scale_fill_manual(values = cbbPalette)

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
m6A_toBasal_collapse <- data.frame( condition = names( tapply(m6A_toBasal$m6AvsA, m6A_toBasal$condition, function(x) sqrt(var(x)/length(x)) ) ), 
                                    ratio = tapply(m6A_toBasal$m6AvsA, m6A_toBasal$condition, mean ), 
                                    sem = tapply(m6A_toBasal$m6AvsA, m6A_toBasal$condition, function(x) sqrt(var(x)/length(x)) )
                                    )
ggplot( m6A_toBasal_collapse, aes( x= condition, y = ratio*100 , fill = condition) )+geom_bar(stat = "identity", color = "black")+ylab("m6A/A Ratio \n(% to Basal condition)")+geom_errorbar(aes(ymin = (ratio-sem)*100, ymax = (ratio+sem)*100 ) , width = 0.5, position=position_dodge(0.9)  )+geom_abline(intercept = 100, slope = 0, lty = "dashed", colour ="black"  )+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 14, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =14, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =14), legend.position = "none" , panel.grid = element_blank()) +scale_fill_manual(values = cbbPalette)

Version Author Date
ba4b639 scottzijiezhang 2020-08-15

I have use two types of plots above. In the boxplot, the lower and upper hinges correspond to the first and third quartiles. The horizontal line indicates the median and the whiskers correspond to the value no further than 1.5× the interquartile range. In the barplot, the error bars are standard error of mean computed from distribution of means (from technical replicates).

m6A_toBasal_glu <- dplyr::filter(all_m6A_new, condition %in% c("Glucose 19mM", "Palmitate 500uM") )
m6A_toBasal_glu$m6AvsA <- m6A_toBasal_glu$m6AvsA/rep( dplyr::filter(all_m6A_new, condition =="Basal" )$m6AvsA, c(2,2,2,2) )

m6A_toBasal_glu$condition <- factor(m6A_toBasal_glu$condition, levels = c("Glucose 19mM", "Palmitate 500uM") )

ggplot( m6A_toBasal_glu, aes( x= condition, y = m6AvsA*100 , fill = condition) )+geom_boxplot(size = 0.9)+ylab("m6A/A Ratio \n(% to Basal condition)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 14, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =14, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =14), legend.position = "none" , panel.grid = element_blank())+geom_abline(intercept = mean(all_m6A$m6AvsA[all_m6A$condition == "Basal"])*100, slope = 0, lty = "dashed", colour ="#00BFC4"  )+scale_fill_manual(values = cbbPalette)

m6A_toBasal_glu_collapse <- data.frame( condition = names( tapply(m6A_toBasal_glu$m6AvsA, m6A_toBasal_glu$condition, function(x) sqrt(var(x)/length(x)) ) ), 
                                    ratio = tapply(m6A_toBasal_glu$m6AvsA, m6A_toBasal_glu$condition, mean ), 
                                    sem = tapply(m6A_toBasal_glu$m6AvsA, m6A_toBasal_glu$condition, function(x) sqrt(var(x)/length(x)) )
                                    )
ggplot( m6A_toBasal_glu_collapse, aes( x= condition, y = ratio*100 , fill = condition) )+geom_bar(stat = "identity", color = "black")+ylab("m6A/A Ratio \n(% to Basal condition)")+geom_errorbar(aes(ymin = (ratio-sem)*100, ymax = (ratio+sem)*100 ) , width = 0.5, position=position_dodge(0.9)  )+geom_abline(intercept = 100, slope = 0, lty = "dashed", colour ="black"  )+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 14, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =14, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =14), legend.position = "none" , panel.grid = element_blank()) +scale_fill_manual(values = cbbPalette)

Paired comparasion

I used paired t-test for statistics of paired comparison.

library(ggpubr)
cbbPalette <- c( "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

basalILIN <- dplyr::filter(all_m6A_new, condition %in% c("Basal","IL1b+INFa") )
basalILIN <- basalILIN[order(  basalILIN$condition), ]

ggplot(data =  basalILIN, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

Version Author Date
ba4b639 scottzijiezhang 2020-08-15
ce13e24 scottzijiezhang 2020-08-13 
DT::datatable( compare_means(m6AvsA ~ condition, data = basalILIN, 
               paired = TRUE , method = "t.test"), rownames = FALSE )
basalILINg <- dplyr::filter(all_m6A_new, condition %in% c("Basal","IL1b+INFg") )


ggplot(data =  basalILINg, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
DT::datatable( compare_means(m6AvsA ~ condition, data = basalILINg, 
               paired = TRUE , method = "t.test"), rownames = FALSE )
basalINa <- dplyr::filter(all_m6A_new, condition %in% c("Basal","INFa") )


ggplot(data =  basalINa, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
DT::datatable( compare_means(m6AvsA ~ condition, data = basalINa, 
               paired = TRUE, method = "t.test"), rownames = FALSE )
basalILb <- dplyr::filter(all_m6A_new, condition %in% c("Basal","IL1b") )


ggplot(data =  basalILb, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
DT::datatable( compare_means(m6AvsA ~ condition, data = basalILb, 
               paired = TRUE, method = "t.test"), rownames = FALSE )
basalTNFa <- dplyr::filter(all_m6A_new, condition %in% c("Basal","TNFa") )


ggplot(data =  basalTNFa, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

Version Author Date
ce13e24 scottzijiezhang 2020-08-13 
DT::datatable( compare_means(m6AvsA ~ condition, data = basalTNFa, 
               paired = TRUE, method = "t.test"), rownames = FALSE )
basalGlucose <- dplyr::filter(all_m6A_new, condition %in% c("Basal","Glucose 19mM") )


ggplot(data =  basalGlucose, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

DT::datatable( compare_means(m6AvsA ~ condition, data = basalGlucose, 
               paired = TRUE, method = "t.test"), rownames = FALSE )
basalPalmitate <- dplyr::filter(all_m6A_new, condition %in% c("Basal","Palmitate 500uM") )


ggplot(data =  basalPalmitate, mapping = aes( x= condition, y = m6AvsA*100 , color = sample, shape = condition, group = sample ) )+geom_line( size = 1)+geom_point( size = 3 )+
  ylab("m6A/A Ratio (%)")+
   theme_bw()  + xlab("Condition")+theme(axis.ticks = element_line(color = "black"), panel.border = element_blank(), 
                     panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), axis.text.x = element_text( size = 15, colour = "black",hjust = 0.9, angle = 30 ),axis.text.y = element_text( size =15, color = "black"),axis.title.x = element_blank(), axis.title.y = element_text( size =15),  panel.grid = element_blank(), legend.text = element_text(size = 15), legend.title = element_text(size = 15) )+scale_color_manual(values = cbbPalette[-c(4)] )+scale_shape_discrete(guide = FALSE)

DT::datatable( compare_means(m6AvsA ~ condition, data = basalPalmitate, 
               paired = TRUE, method = "t.test"), rownames = FALSE )

sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.10

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggsci_2.9     ggpubr_0.2    magrittr_1.5  ggplot2_3.1.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1       later_0.8.0      compiler_3.5.3   pillar_1.3.1    
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.3.0  tools_3.5.3     
 [9] digest_0.6.18    jsonlite_1.6     evaluate_0.13    tibble_2.1.1    
[13] gtable_0.3.0     pkgconfig_2.0.2  rlang_0.4.0      shiny_1.3.2     
[17] crosstalk_1.0.0  yaml_2.2.0       xfun_0.6         withr_2.1.2     
[21] stringr_1.4.0    dplyr_0.8.0.1    knitr_1.22       fs_1.3.0        
[25] htmlwidgets_1.3  rprojroot_1.3-2  grid_3.5.3       DT_0.5.1        
[29] tidyselect_0.2.5 glue_1.3.1       R6_2.4.0         rmarkdown_1.12  
[33] tidyr_0.8.3      purrr_0.3.2      whisker_0.3-2    promises_1.0.1  
[37] backports_1.1.4  scales_1.0.0     htmltools_0.3.6  assertthat_0.2.1
[41] xtable_1.8-4     mime_0.6         colorspace_1.4-1 httpuv_1.5.1    
[45] labeling_0.3     stringi_1.4.3    lazyeval_0.2.2   munsell_0.5.0   
[49] crayon_1.3.4