Distribution Analysis
Ann Wells
April 23, 2023
Introduction and Data files
R Markdown
This dataset contains nine tissues (heart, hippocampus, hypothalamus, kidney, liver, prefrontal cortex, skeletal muscle, small intestine, and spleen) from C57BL/6J mice that were fed 2-deoxyglucose (6g/L) through their drinking water for 96hrs or 4wks. Organs from mice were harvested and processed for metabolomics and transcriptomics. The data in this document pertains to the transcriptomics data only. This document specifically calculates and plots the distributions for log transformed data. The counts that were used were FPKM normalized before being log transformed.
needed.packages <- c("tidyverse", "here", "functional", "gplots", "dplyr", "GeneOverlap", "R.utils", "reshape2","magrittr","data.table", "RColorBrewer","preprocessCore")
for(i in 1:length(needed.packages)){library(needed.packages[i], character.only = TRUE)}Read in data
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))Boxplots
These plots will show the distribution of the data by displaying the variability or dispersion of the data.
Time
This boxplot groups control and treated samples for each time point and tissue.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = Tissue, y = value, fill = Time)) + geom_boxplot() + xlab("") + ylab(expression(log(count + 1))) + scale_fill_manual(values = c("darkred", "dodgerblue")) + theme(axis.text.x = element_text(angle = 45))
Tissue
This boxplot groups control and treated samples for each time point and tissue but displays the data by time point.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = Time, y = value, fill = Tissue)) + geom_boxplot() + xlab("") + ylab(expression(log(count + 1))) + theme(axis.text.x = element_text(angle = 45))
Treatment
This boxplot groups time points for each treatment and tissue.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = Tissue, y = value, fill = Treatment)) + geom_boxplot() + xlab("") + ylab(expression(log(count + 1))) + scale_fill_manual(values = c("darkred", "dodgerblue")) + theme(axis.text.x = element_text(angle = 45))
Treatment by tissue by time
This boxplot displays treatment and time for each tissue.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = Tissue, y = value, fill = Tissue)) + geom_boxplot() + xlab("") + ylab(expression(log(count + 1))) + theme(axis.text.x = element_text(angle = 45)) + facet_wrap(~ Time*Treatment) + theme_dark() + theme(strip.background = element_rect(color="black", size=1.5, linetype="solid")) + theme(axis.text.x = element_text(angle = 90, face = "bold", size = 8,hjust=0.95,vjust=0.2))
Samples
This boxplot displays all of the samples but they are colored by tissue.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = rowname, y = value, fill = Tissue)) + geom_boxplot() + xlab("") + ylab(expression(log(count + 1))) + theme(legend.position='none') + theme(axis.text.x = element_text(angle = 90, face = "bold", size = 4))
Density Plots
Density plots show us the distribution of the data.
Time by treatment
This plot displays treatment and time for each tissue for each sample.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = value, colour = rowname, fill = rowname)) + ylim(c(0, 0.5)) +
geom_density(alpha = 0.05, size = .5) + facet_wrap(~ Time*Treatment) + xlab(expression(log(count + 1))) + theme_dark() + theme(strip.background = element_rect(color="black", size=1.5, linetype="solid")) + theme(legend.position = "none")
Tissue
This plot groups treatment and time point for each tissue for each sample.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.tdata.FPKM.sample.info <- rownames_to_column(as.data.frame(log.tdata.FPKM.sample.info))
melttissue <- melt(log.tdata.FPKM.sample.info, variable_name="Tissue")
ggplot(melttissue, aes(x = value, colour = Treatment, fill = Time)) + ylim(c(0, 0.5)) +
geom_density(alpha = 0.05, size = .5) + facet_wrap(~ Tissue) + xlab(expression(log(count + 1))) + theme_dark() + theme(strip.background = element_rect(color="black", size=1.5, linetype="solid")) + theme(legend.position = "none")
Clustering
This heatmap clusters the data for each sample to determine, which samples are most closely related through their expression.
tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))
log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])
log.data.FPKM.sample.info <- t(sapply(log.tdata.FPKM.sample.info, as.numeric))
colnames(log.data.FPKM.sample.info) <- rownames(log.tdata.FPKM.sample.info)
mat.dist = log.data.FPKM.sample.info
colnames(mat.dist) = paste(colnames(mat.dist), tdata.FPKM.sample.info$Time, tdata.FPKM.sample.info$Treatment, tdata.FPKM.sample.info$Tissue, sep = " : ")
mat.dist = as.matrix(dist(t(mat.dist)))
mat.dist = mat.dist/max(mat.dist)
hmcol = colorRampPalette(brewer.pal(9, "GnBu"))(144)
pheatmap::pheatmap(mat.dist, color = rev(hmcol), fontsize = 2)
MA plots
MA plots plot the log-fold change against the log average. The M in the plot is the log of the ratio of counts for each gene between two samples. The A in the plot is the average count for each gene across two samples. Each comparison below is only plotting like with like (i.e. spleen 2DG 4wks with spleen 2DG 4wks). These plots will should hover around zero to visualize that samples within the same treatment and time combination have the same expression profile, relatively.
MA plot for Spleen 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Spleen" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
p <- c()
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
##M-values
M=x-y ##A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(p))
cat("\n \n")
}
}MA plot A019 - A019
MA plot A019 - A028
MA plot A019 - A091
MA plot A019 - A172
MA plot A028 - A019
MA plot A028 - A028
MA plot A028 - A091
MA plot A028 - A172
MA plot A091 - A019
MA plot A091 - A028
MA plot A091 - A091
MA plot A091 - A172
MA plot A172 - A019
MA plot A172 - A028
MA plot A172 - A091
MA plot A172 - A172
MA plot for Spleen control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Spleen" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat(' \n\n')
}
}MA plot A001 - A001
MA plot A001 - A037
MA plot A001 - A046
MA plot A001 - A082
MA plot A037 - A001
MA plot A037 - A037
MA plot A037 - A046
MA plot A037 - A082
MA plot A046 - A001
MA plot A046 - A037
MA plot A046 - A046
MA plot A046 - A082
MA plot A082 - A001
MA plot A082 - A037
MA plot A082 - A046
MA plot A082 - A082
MA plot for Spleen 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Spleen" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A010 - A010
MA plot A010 - A136
MA plot A010 - A145
MA plot A010 - A163
MA plot A136 - A010
MA plot A136 - A136
MA plot A136 - A145
MA plot A136 - A163
MA plot A145 - A010
MA plot A145 - A136
MA plot A145 - A145
MA plot A145 - A163
MA plot A163 - A010
MA plot A163 - A136
MA plot A163 - A145
MA plot A163 - A163
MA plot for Spleen control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Spleen" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A055 - A055
MA plot A055 - A064
MA plot A055 - A073
MA plot A055 - A109
MA plot A064 - A055
MA plot A064 - A064
MA plot A064 - A073
MA plot A064 - A109
MA plot A073 - A055
MA plot A073 - A064
MA plot A073 - A073
MA plot A073 - A109
MA plot A109 - A055
MA plot A109 - A064
MA plot A109 - A073
MA plot A109 - A109
MA plot for Kidney 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Kidney" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A020 - A020
MA plot A020 - A029
MA plot A020 - A092
MA plot A020 - A173
MA plot A029 - A020
MA plot A029 - A029
MA plot A029 - A092
MA plot A029 - A173
MA plot A092 - A020
MA plot A092 - A029
MA plot A092 - A092
MA plot A092 - A173
MA plot A173 - A020
MA plot A173 - A029
MA plot A173 - A092
MA plot A173 - A173
MA plot for Kidney control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Kidney" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A002 - A002
MA plot A002 - A038
MA plot A002 - A047
MA plot A002 - A083
MA plot A038 - A002
MA plot A038 - A038
MA plot A038 - A047
MA plot A038 - A083
MA plot A047 - A002
MA plot A047 - A038
MA plot A047 - A047
MA plot A047 - A083
MA plot A083 - A002
MA plot A083 - A038
MA plot A083 - A047
MA plot A083 - A083
MA plot for Kidney 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Kidney" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A011 - A011
MA plot A011 - A137
MA plot A011 - A146
MA plot A011 - A164
MA plot A137 - A011
MA plot A137 - A137
MA plot A137 - A146
MA plot A137 - A164
MA plot A146 - A011
MA plot A146 - A137
MA plot A146 - A146
MA plot A146 - A164
MA plot A164 - A011
MA plot A164 - A137
MA plot A164 - A146
MA plot A164 - A164
MA plot for Kidney control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Kidney" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A056 - A056
MA plot A056 - A065
MA plot A056 - A074
MA plot A056 - A110
MA plot A065 - A056
MA plot A065 - A065
MA plot A065 - A074
MA plot A065 - A110
MA plot A074 - A056
MA plot A074 - A065
MA plot A074 - A074
MA plot A074 - A110
MA plot A110 - A056
MA plot A110 - A065
MA plot A110 - A074
MA plot A110 - A110
MA plot for Hypothalamus 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hypothanamus" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A025 - A025
MA plot A025 - A034
MA plot A025 - A097
MA plot A025 - A178
MA plot A034 - A025
MA plot A034 - A034
MA plot A034 - A097
MA plot A034 - A178
MA plot A097 - A025
MA plot A097 - A034
MA plot A097 - A097
MA plot A097 - A178
MA plot A178 - A025
MA plot A178 - A034
MA plot A178 - A097
MA plot A178 - A178
MA plot for Hypothalamus control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hypothanamus" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A007 - A007
MA plot A007 - A043
MA plot A007 - A052
MA plot A007 - A088
MA plot A043 - A007
MA plot A043 - A043
MA plot A043 - A052
MA plot A043 - A088
MA plot A052 - A007
MA plot A052 - A043
MA plot A052 - A052
MA plot A052 - A088
MA plot A088 - A007
MA plot A088 - A043
MA plot A088 - A052
MA plot A088 - A088
MA plot for Hypothalamus 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hypothanamus" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A016 - A016
MA plot A016 - A142
MA plot A016 - A151
MA plot A016 - A169
MA plot A142 - A016
MA plot A142 - A142
MA plot A142 - A151
MA plot A142 - A169
MA plot A151 - A016
MA plot A151 - A142
MA plot A151 - A151
MA plot A151 - A169
MA plot A169 - A016
MA plot A169 - A142
MA plot A169 - A151
MA plot A169 - A169
MA plot for Hypothalamus control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hypothanamus" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A061 - A061
MA plot A061 - A070
MA plot A061 - A079
MA plot A061 - A115
MA plot A070 - A061
MA plot A070 - A070
MA plot A070 - A079
MA plot A070 - A115
MA plot A079 - A061
MA plot A079 - A070
MA plot A079 - A079
MA plot A079 - A115
MA plot A115 - A061
MA plot A115 - A070
MA plot A115 - A079
MA plot A115 - A115
MA plot for Hippocampus 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hippocampus" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A026 - A026
MA plot A026 - A035
MA plot A026 - A098
MA plot A026 - A179
MA plot A035 - A026
MA plot A035 - A035
MA plot A035 - A098
MA plot A035 - A179
MA plot A098 - A026
MA plot A098 - A035
MA plot A098 - A098
MA plot A098 - A179
MA plot A179 - A026
MA plot A179 - A035
MA plot A179 - A098
MA plot A179 - A179
MA plot for Hippocampus control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hippocampus" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A008 - A008
MA plot A008 - A044
MA plot A008 - A053
MA plot A008 - A089
MA plot A044 - A008
MA plot A044 - A044
MA plot A044 - A053
MA plot A044 - A089
MA plot A053 - A008
MA plot A053 - A044
MA plot A053 - A053
MA plot A053 - A089
MA plot A089 - A008
MA plot A089 - A044
MA plot A089 - A053
MA plot A089 - A089
MA plot for Hippocampus 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hippocampus" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A017 - A017
MA plot A017 - A143
MA plot A017 - A152
MA plot A017 - A170
MA plot A143 - A017
MA plot A143 - A143
MA plot A143 - A152
MA plot A143 - A170
MA plot A152 - A017
MA plot A152 - A143
MA plot A152 - A152
MA plot A152 - A170
MA plot A170 - A017
MA plot A170 - A143
MA plot A170 - A152
MA plot A170 - A170
MA plot for Hippocampus control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Hippocampus" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A062 - A062
MA plot A062 - A071
MA plot A062 - A080
MA plot A062 - A116
MA plot A071 - A062
MA plot A071 - A071
MA plot A071 - A080
MA plot A071 - A116
MA plot A080 - A062
MA plot A080 - A071
MA plot A080 - A080
MA plot A080 - A116
MA plot A116 - A062
MA plot A116 - A071
MA plot A116 - A080
MA plot A116 - A116
MA plot for Liver 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Liver" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A021 - A021
MA plot A021 - A030
MA plot A021 - A093
MA plot A021 - A174
MA plot A030 - A021
MA plot A030 - A030
MA plot A030 - A093
MA plot A030 - A174
MA plot A093 - A021
MA plot A093 - A030
MA plot A093 - A093
MA plot A093 - A174
MA plot A174 - A021
MA plot A174 - A030
MA plot A174 - A093
MA plot A174 - A174
MA plot for Liver control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Liver" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A003 - A003
MA plot A003 - A039
MA plot A003 - A048
MA plot A003 - A084
MA plot A039 - A003
MA plot A039 - A039
MA plot A039 - A048
MA plot A039 - A084
MA plot A048 - A003
MA plot A048 - A039
MA plot A048 - A048
MA plot A048 - A084
MA plot A084 - A003
MA plot A084 - A039
MA plot A084 - A048
MA plot A084 - A084
MA plot for Liver 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Liver" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A012 - A012
MA plot A012 - A138
MA plot A012 - A147
MA plot A012 - A165
MA plot A138 - A012
MA plot A138 - A138
MA plot A138 - A147
MA plot A138 - A165
MA plot A147 - A012
MA plot A147 - A138
MA plot A147 - A147
MA plot A147 - A165
MA plot A165 - A012
MA plot A165 - A138
MA plot A165 - A147
MA plot A165 - A165
MA plot for Liver control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Liver" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A057 - A057
MA plot A057 - A066
MA plot A057 - A075
MA plot A057 - A111
MA plot A066 - A057
MA plot A066 - A066
MA plot A066 - A075
MA plot A066 - A111
MA plot A075 - A057
MA plot A075 - A066
MA plot A075 - A075
MA plot A075 - A111
MA plot A111 - A057
MA plot A111 - A066
MA plot A111 - A075
MA plot A111 - A111
MA plot for Heart 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Heart" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
if(i != j){
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}
}MA plot A022 - A031
MA plot A022 - A094
MA plot A022 - A175
MA plot A031 - A022
MA plot A031 - A094
MA plot A031 - A175
MA plot A094 - A022
MA plot A094 - A031
MA plot A094 - A175
MA plot A175 - A022
MA plot A175 - A031
MA plot A175 - A094
MA plot for Heart control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Heart" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A004 - A004
MA plot A004 - A040
MA plot A004 - A049
MA plot A004 - A085
MA plot A040 - A004
MA plot A040 - A040
MA plot A040 - A049
MA plot A040 - A085
MA plot A049 - A004
MA plot A049 - A040
MA plot A049 - A049
MA plot A049 - A085
MA plot A085 - A004
MA plot A085 - A040
MA plot A085 - A049
MA plot A085 - A085
MA plot for Heart 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Heart" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A013 - A013
MA plot A013 - A139
MA plot A013 - A148
MA plot A013 - A166
MA plot A139 - A013
MA plot A139 - A139
MA plot A139 - A148
MA plot A139 - A166
MA plot A148 - A013
MA plot A148 - A139
MA plot A148 - A148
MA plot A148 - A166
MA plot A166 - A013
MA plot A166 - A139
MA plot A166 - A148
MA plot A166 - A166
MA plot for Heart control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Heart" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
##M-values
M=x-y ##A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A058 - A058
MA plot A058 - A067
MA plot A058 - A076
MA plot A058 - A112
MA plot A067 - A058
MA plot A067 - A067
MA plot A067 - A076
MA plot A067 - A112
MA plot A076 - A058
MA plot A076 - A067
MA plot A076 - A076
MA plot A076 - A112
MA plot A112 - A058
MA plot A112 - A067
MA plot A112 - A076
MA plot A112 - A112
MA plot for Small Intestine 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Small Intestine" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A024 - A024
MA plot A024 - A033
MA plot A024 - A096
MA plot A024 - A177
MA plot A033 - A024
MA plot A033 - A033
MA plot A033 - A096
MA plot A033 - A177
MA plot A096 - A024
MA plot A096 - A033
MA plot A096 - A096
MA plot A096 - A177
MA plot A177 - A024
MA plot A177 - A033
MA plot A177 - A096
MA plot A177 - A177
MA plot for Small Intestine control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Small Intestine" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A006 - A006
MA plot A006 - A042
MA plot A006 - A051
MA plot A006 - A087
MA plot A042 - A006
MA plot A042 - A042
MA plot A042 - A051
MA plot A042 - A087
MA plot A051 - A006
MA plot A051 - A042
MA plot A051 - A051
MA plot A051 - A087
MA plot A087 - A006
MA plot A087 - A042
MA plot A087 - A051
MA plot A087 - A087
MA plot for Small Intestine 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Small Intestine" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A015 - A015
MA plot A015 - A141
MA plot A015 - A150
MA plot A015 - A168
MA plot A141 - A015
MA plot A141 - A141
MA plot A141 - A150
MA plot A141 - A168
MA plot A150 - A015
MA plot A150 - A141
MA plot A150 - A150
MA plot A150 - A168
MA plot A168 - A015
MA plot A168 - A141
MA plot A168 - A150
MA plot A168 - A168
MA plot for Small Intestine control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Small Intestine" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A060 - A060
MA plot A060 - A069
MA plot A060 - A078
MA plot A060 - A114
MA plot A069 - A060
MA plot A069 - A069
MA plot A069 - A078
MA plot A069 - A114
MA plot A078 - A060
MA plot A078 - A069
MA plot A078 - A078
MA plot A078 - A114
MA plot A114 - A060
MA plot A114 - A069
MA plot A114 - A078
MA plot A114 - A114
MA plot for Skeletal Muscle 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Skeletal Muscle" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A023 - A023
MA plot A023 - A032
MA plot A023 - A095
MA plot A023 - A176
MA plot A032 - A023
MA plot A032 - A032
MA plot A032 - A095
MA plot A032 - A176
MA plot A095 - A023
MA plot A095 - A032
MA plot A095 - A095
MA plot A095 - A176
MA plot A176 - A023
MA plot A176 - A032
MA plot A176 - A095
MA plot A176 - A176
MA plot for Skeletal Muscle control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Skeletal Muscle" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A005 - A005
MA plot A005 - A041
MA plot A005 - A050
MA plot A005 - A086
MA plot A041 - A005
MA plot A041 - A041
MA plot A041 - A050
MA plot A041 - A086
MA plot A050 - A005
MA plot A050 - A041
MA plot A050 - A050
MA plot A050 - A086
MA plot A086 - A005
MA plot A086 - A041
MA plot A086 - A050
MA plot A086 - A086
MA plot for Skeletal Muscle 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Skeletal Muscle" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A014 - A014
MA plot A014 - A140
MA plot A014 - A149
MA plot A014 - A167
MA plot A140 - A014
MA plot A140 - A140
MA plot A140 - A149
MA plot A140 - A167
MA plot A149 - A014
MA plot A149 - A140
MA plot A149 - A149
MA plot A149 - A167
MA plot A167 - A014
MA plot A167 - A140
MA plot A167 - A149
MA plot A167 - A167
MA plot for Skeletal Muscle control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Skeletal Muscle" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A059 - A059
MA plot A059 - A068
MA plot A059 - A077
MA plot A059 - A113
MA plot A068 - A059
MA plot A068 - A068
MA plot A068 - A077
MA plot A068 - A113
MA plot A077 - A059
MA plot A077 - A068
MA plot A077 - A077
MA plot A077 - A113
MA plot A113 - A059
MA plot A113 - A068
MA plot A113 - A077
MA plot A113 - A113
MA plot for Prefrontal Cortex 2DG 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Pre-frontal Cortex" & Treatment == "2DG" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A027 - A027
MA plot A027 - A036
MA plot A027 - A099
MA plot A027 - A180
MA plot A036 - A027
MA plot A036 - A036
MA plot A036 - A099
MA plot A036 - A180
MA plot A099 - A027
MA plot A099 - A036
MA plot A099 - A099
MA plot A099 - A180
MA plot A180 - A027
MA plot A180 - A036
MA plot A180 - A099
MA plot A180 - A180
MA plot for Prefrontal Cortex control 4wks
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Pre-frontal Cortex" & Treatment == "None" & Time == "4 wks") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A009 - A009
MA plot A009 - A045
MA plot A009 - A054
MA plot A009 - A090
MA plot A045 - A009
MA plot A045 - A045
MA plot A045 - A054
MA plot A045 - A090
MA plot A054 - A009
MA plot A054 - A045
MA plot A054 - A054
MA plot A054 - A090
MA plot A090 - A009
MA plot A090 - A045
MA plot A090 - A054
MA plot A090 - A090
MA plot for Prefrontal Cortex 2DG 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Pre-frontal Cortex" & Treatment == "2DG" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A018 - A018
MA plot A018 - A144
MA plot A018 - A153
MA plot A018 - A171
MA plot A144 - A018
MA plot A144 - A144
MA plot A144 - A153
MA plot A144 - A171
MA plot A153 - A018
MA plot A153 - A144
MA plot A153 - A153
MA plot A153 - A171
MA plot A171 - A018
MA plot A171 - A144
MA plot A171 - A153
MA plot A171 - A171
MA plot for Prefrontal Cortex control 96hrs
subdata <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue =="Pre-frontal Cortex" & Treatment == "None" & Time == "96 hrs") %>% column_to_rownames()
subdata <- subdata[,-c(27237:27239)]
log.data.FPKM.sample.info <- t(subdata)
for(i in 1:ncol(log.data.FPKM.sample.info)){
for(j in 1:ncol(log.data.FPKM.sample.info)){
x = log.data.FPKM.sample.info[,i]
y = log.data.FPKM.sample.info[,j]
## M-values
M=x-y ## A-values
A = (x + y)/2
df = data.frame(A, M)
cat('###',"MA plot", colnames(log.data.FPKM.sample.info)[[i]], "-", colnames(log.data.FPKM.sample.info)[[j]] ,' \n')
p <- ggplot(df, aes(x = A, y = M)) + geom_point(size = 1.5, alpha = 1/5) +
geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + theme_bw()
print(p)
#print(htmltools::tagList(ggplotly(p)))
cat("\n \n")
}
}MA plot A063 - A063
MA plot A063 - A072
MA plot A063 - A081
MA plot A063 - A117
MA plot A072 - A063
MA plot A072 - A072
MA plot A072 - A081
MA plot A072 - A117
MA plot A081 - A063
MA plot A081 - A072
MA plot A081 - A081
MA plot A081 - A117
MA plot A117 - A063
MA plot A117 - A072
MA plot A117 - A081
MA plot A117 - A117
Analysis performed by Ann Wells
The Carter Lab The Jackson Laboratory 2023
ann.wells@jax.org