my.C2C12.length.v1 <- read.table('~/Desktop/TEST/C2C12_MB_H3K4me3_vs_expressionREDUX.bed', header=FALSE, sep="\t")
peak_score <- my.C2C12.length.v1[,10]
broad.ids <- getBroadIds.2 (my.C2C12.length.v1,0.95)
my.C2C12.length.v1.redux <- my.C2C12.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.C2C12.length.v1.redux[,5]
peak_score.2 <- my.C2C12.length.v1.redux[,10]
my.exp.broad.C2C12 <- exp_level.2[broad.ids]
my.exp.nonbroad.C2C12 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.C2C12,my.exp.nonbroad.C2C12),outline = FALSE, names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "C2C12 MB")
getBroadIds.2<- function(iso, thr){
peak_length <- iso[,8] - iso[,7]
Fn <- ecdf(peak_length)
my.full.quartiles <- Fn(peak_length)
broad.ids <- which(my.full.quartiles >= thr)
broad.ids
}
peak_score <- my.C2C12.length.v1[,10]
broad.ids <- getBroadIds.2 (my.C2C12.length.v1,0.95)
my.C2C12.length.v1.redux <- my.C2C12.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.C2C12.length.v1.redux[,5]
peak_score.2 <- my.C2C12.length.v1.redux[,10]
my.exp.broad.C2C12 <- exp_level.2[broad.ids]
my.exp.nonbroad.C2C12 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.C2C12,my.exp.nonbroad.C2C12),outline = FALSE, names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "C2C12 MB")
head(my.C2C12.length.v1)
peak_length.C2C12 <- my.C2C12.length.v1[,3] - my.C2C12.length.v1[,2]
Fn <- ecdf(peak_length.C2C12)
my.full.quartiles <- Fn(peak_length.C2C12)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
dim(my.C2C12.length.v1)
length(exp_level.2)
peak_length.C2C12 <- my.C2C12.length.v1[-which(peak_score %in% -1),3] - my.C2C12.length.v1[-which(peak_score %in% -1),2]
Fn <- ecdf(peak_length.C2C12)
my.full.quartiles <- Fn(peak_length.C2C12)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.C2C12 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(log10(peak_length.C2C12[-my.null]),log10(exp_level.2[-my.null]))
plot(my.data.pos,col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.H1.length.v1 <- read.table('~/Desktop/TEST/H1_expressed_H3K4me3_intersect.bed', header=FALSE, sep="\t")
peak_score <- my.H1.length.v1[,10]
broad.ids <- getBroadIds.2 (my.H1.length.v1,0.95)
my.H1.length.v1.redux <- my.H1.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.H1.length.v1.redux[,5]
peak_score.2 <- my.H1.length.v1.redux[,10]
my.exp.broad.H1 <- exp_level.2[broad.ids]
my.exp.nonbroad.H1 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.H1,my.exp.nonbroad.H1),outline = FALSE, names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "H1 MB")
peak_length.H1 <- my.H1.length.v1[-which(peak_score %in% -1),3] - my.H1.length.v1[-which(peak_score %in% -1),2]
Fn <- ecdf(peak_length.H1)
my.full.quartiles <- Fn(peak_length.H1)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.H1 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.H1.length.v1
my.H1.length.v1[1:10,]
peak_length.H1 <- my.H1.length.v1[-which(peak_score %in% -1),8] - my.H1.length.v1[-which(peak_score %in% -1),7]
Fn <- ecdf(peak_length.H1)
my.full.quartiles <- Fn(peak_length.H1)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.H1 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
cor(log10(peak_length.H1[-my.null]),log10(exp_level.2[-my.null]),method="spearman")
cor(my.data.pos[,1],my.data.pos[,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.4)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.5)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.6)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.2)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.3)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.2)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.3)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.4)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.5)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.6)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.6)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] > 0.7)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
my.sel <- which(my.data.pos[,1] < 0.7)
cor(my.data.pos[my.sel,1],my.data.pos[my.sel,2],method="spearman")
#0.2300984
my.H1.length.v1 <- read.table('~/Desktop/TEST/H1_expressed_H3K4me3_intersect.KNOWN.bedbed', header=FALSE, sep="\t")
my.H1.length.v1 <- read.table('~/Desktop/TEST/H1_expressed_H3K4me3_intersect.KNOWN.bed', header=FALSE, sep="\t")
peak_score <- my.H1.length.v1[,10]
broad.ids <- getBroadIds.2 (my.H1.length.v1,0.95)
my.H1.length.v1.redux <- my.H1.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.H1.length.v1.redux[,5]
peak_score.2 <- my.H1.length.v1.redux[,10]
my.exp.broad.H1 <- exp_level.2[broad.ids]
my.exp.nonbroad.H1 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.H1,my.exp.nonbroad.H1),outline = FALSE,
names=c("Broad", "Non-broad"), ylab = "Expression level (fkpm)",
main = "H1")
peak_length.H1 <- my.H1.length.v1[-which(peak_score %in% -1),8] - my.H1.length.v1[-which(peak_score %in% -1),7]
Fn <- ecdf(peak_length.H1)
my.full.quartiles <- Fn(peak_length.H1)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.H1 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(log10(peak_length.H1[-my.null]),log10(exp_level.2[-my.null]))
plot(my.data.pos,col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
cor(my.data.pos[,1],my.data.pos[,2],method="spearman")
my.data.pos <- cbind(log10(peak_length.H1[-my.null]),log10(exp_level.2[-my.null]))
plot(my.data.pos,col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(log10(peak_length.H1[-my.null]),log10(exp_level.2[-my.null]))
plot(my.data.pos,col=rgb(205,51,1,30,maxColorValue=255), pch=16)
cor(my.data.pos[,1],my.data.pos[,2],method="spearman")
#0.3228654
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.H1 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.C2C12.length.v1 <- read.table('~/Desktop/TEST/C2C12_MT_H3K4me3_vs_expressionREDUX.bed', header=FALSE, sep="\t")
peak_score <- my.C2C12.length.v1[,10]
head(my.C2C12.length.v1)
my.C2C12.length.v1 <- read.table('~/Desktop/TEST/C2C12_MT_H3K4me3_vs_expressionREDUX.bed', header=FALSE, sep="\t")
peak_score <- my.C2C12.length.v1[,10]
head(my.C2C12.length.v1)
peak_score <- my.C2C12.length.v1[,7]
broad.ids <- getBroadIds.2 (my.C2C12.length.v1,0.95)
my.C2C12.length.v1.redux <- my.C2C12.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.C2C12.length.v1.redux[,5]
peak_score.2 <- my.C2C12.length.v1.redux[,10]
my.exp.broad.C2C12 <- exp_level.2[broad.ids]
my.exp.nonbroad.C2C12 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.C2C12,my.exp.nonbroad.C2C12),outline = FALSE, names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "C2C12 MB")
boxplot(cbind(my.exp.broad.C2C12,my.exp.nonbroad.C2C12),outline = FALSE,
names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "C2C12 MT")
peak_length.C2C12 <- my.C2C12.length.v1[-which(peak_score %in% -1),8] - my.C2C12.length.v1[-which(peak_score %in% -1),7]
Fn <- ecdf(peak_length.C2C12)
my.full.quartiles <- Fn(peak_length.C2C12)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.C2C12 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(log10(peak_length.C2C12[-my.null]),log10(exp_level.2[-my.null]))
plot(my.data.pos,col=rgb(205,51,1,30,maxColorValue=255), pch=16)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.C2C12 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
cor(my.full.quartiles[-my.null],exp_level.2[-my.null],method="spearman")
my.C2C12.length.v1 <- read.table('~/Desktop/TEST/C2C12_MB_H3K4me3_vs_expressionREDUX.bed', header=FALSE, sep="\t")
peak_score <- my.C2C12.length.v1[,10]
broad.ids <- getBroadIds.2 (my.C2C12.length.v1,0.95)
my.C2C12.length.v1.redux <- my.C2C12.length.v1[-which(peak_score %in% -1),]
exp_level.2 <- my.C2C12.length.v1.redux[,5]
peak_score.2 <- my.C2C12.length.v1.redux[,10]
my.exp.broad.C2C12 <- exp_level.2[broad.ids]
my.exp.nonbroad.C2C12 <- exp_level.2[-broad.ids]
boxplot(cbind(my.exp.broad.C2C12,my.exp.nonbroad.C2C12),outline = FALSE, names=c("Broad", "Non-broad"), ylab = "Expression level in mESCs (fkpm)", main = "C2C12 MB")
peak_length.C2C12 <- my.C2C12.length.v1[-which(peak_score %in% -1),8] - my.C2C12.length.v1[-which(peak_score %in% -1),7]
Fn <- ecdf(peak_length.C2C12)
my.full.quartiles <- Fn(peak_length.C2C12)
my.null <- which(exp_level.2 == 0)
my.data.pos <- cbind(my.full.quartiles[-my.null],exp_level.2[-my.null])
my.broad.C2C12 <- which(my.data.pos[,1] >= 0.95)
plot(my.data.pos,log='y',col=rgb(205,51,1,30,maxColorValue=255), pch=16)
cor(my.full.quartiles[-my.null],exp_level.2[-my.null],method="spearman")
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_Liver_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt')
head(my.data)
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_Liver_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt',
header=T, sep="\t")
head(my.data)
boxplot(my.data$log2FoldChange[my.data$padj < 0.05],my.data$log2FoldChange[my.data$padj < 0.05])
plot(boxplot(my.data$log2FoldChange,my.data$padj)
)
plot(my.data$log2FoldChange,my.data$padj)
plot(my.data$log2FoldChange,-my.data$padj)
plot(my.data$log2FoldChange,-log10(my.data$padj))
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, color = "grey")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "grey")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08))
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
summary(my.data$log2FoldChange[my.data$padj < 0.05])
summary(2^my.data$log2FoldChange[my.data$padj < 0.05])
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
points(my.data$log2FoldChange[my.data$padj < 0.1],-log10(my.data$padj)[my.data$padj < 0.1], cex=0.5, pch = 16, col = "pink")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
summary(my.data$log2FoldChange)
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_NPCs_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt _all_genes_statistics.txt',
header=T, sep="\t")
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_NPCs_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt',
header=T, sep="\t")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
my.data$log2FoldChange
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5)
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_Cerebellum_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt',
header=T, sep="\t")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2016-03-14_SCDE_NPC_aging_differential_expression_analysis_with_pvals.txt',
header=T, sep="\t")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
head(my.data)
plot(my.data$Z,-log10(my.data$p_adj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
plot(my.data$Z,-log10(my.data$p_adj), cex=0.5, col = "darkgrey",
main="scNPC")
main="scNPC")
points(my.data$Z[my.data$p_adj < 0.05],-log10(my.data$p_adj)[my.data$p_adj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
sum(my.data$p_adj < 0.05)
my.data <- read.csv('Downloads/deseq2matricesforupstreamregulatoranalysis/2015-11-19_OlfactoryBulb_DESeq2_LINEAR_model_with_age _all_genes_statistics.txt',
header=T, sep="\t")
plot(my.data$log2FoldChange,-log10(my.data$padj), cex=0.5, col = "darkgrey", xlim = c(-0.08, 0.08),
main="Liver")
points(my.data$log2FoldChange[my.data$padj < 0.05],-log10(my.data$padj)[my.data$padj < 0.05], cex=0.5, pch = 16, col = "red")
abline(v=0, lty = "dashed")
sum(my.data$log2FoldChange == 0)
sum(abs(my.data$log2FoldChange) < 0.005)
length(my.data$log2FoldChange)
sum(abs(my.data$log2FoldChange) < 0.001)
head(sort(log2FoldChange[my.data$padj < 0.05]))
head(sort(my.data$log2FoldChange[my.data$padj < 0.05]))
install.packages("doMC", dep=T)
install.packages("caTools", dep=T)
install.packages("utils", dep=T)
install.packages("utils", dep = T)
install.packages("utils", dep = T)
install.packages("utils", dep = T)
install.packages("utils", dep = T)
source("http://bioconductor.org/biocLite.R")
biocLite( "BSgenome" )
source("http://bioconductor.org/biocLite.R")
biocLite( "BSgenome" )
biocLite( "Rsamtools" )
biocLite( "ShortRead" )
my.data <- read.table('~/Downloads/2017-12-05 _postSVA_longevity_matrix.txt', header = T, sep = "\t")
my.data <- read.csv('~/Downloads/2017-12-05 _postSVA_longevity_matrix.txt', header = T, sep = "\t")
library('pheatmap')
install.packages('pheatmap')a
install.packages('pheatmap')
library('pheatmap')
pheatmap(cor(my.data[,-1]))
cor(my.data[,-1])
pheatmap(cor(my.data[,-1], na.action = 'omit'))
?cor
pheatmap(cor(my.data[,-1], use = "complete.obs", method = "spearman"))
pdf("TEst.pdf",height = 20, width = 20)
pheatmap(cor(my.data[,-1], use = "complete.obs", method = "spearman"), show_colnames = F)
dev.off()
?pheatmap
pdf("TEst.pdf",height = 20, width = 20)
pheatmap(cor(my.data[,-1], use = "complete.obs", method = "spearman"), show_colnames = F, fontsize = 5)
dev.off()
View(my.data)
dim(my.saa)
dim(my.data)
View(my.data)
setwd('/Users/benayoun/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure5_Conservation/Comparisons/GTex/')
options(stringsAsFactors=F)
library(bitops)
source('cross_species_comparison_FUN.R')
# 2017-05-22
# try to compare Human GTEX againg and my data
load("/Users/benayoun/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_cereb_2015-11-19.RData")
load("/Users/benayoun/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_Heart_2015-11-19.RData")
load("/Users/benayoun/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_Liver_2015-11-19.RData")
setwd('/Users/BB_2012/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure5_Conservation/Comparisons/GTex/')
options(stringsAsFactors=F)
library(bitops)
source('cross_species_comparison_FUN.R')
# 2017-05-22
# try to compare Human GTEX againg and my data
load("/Users/BB_2012/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_cereb_2015-11-19.RData")
load("/Users/BB_2012/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_Heart_2015-11-19.RData")
load("/Users/BB_2012/Dropbox/Manuscripts_and_Publications/2018_aging_epigenomics_data_description/Aging_omics_paper/Github_folder/Figure3_Machine_learning/Feature_extraction/Feature_folders/RNAseq_DEseq2_results/RNA_seq_result_Liver_2015-11-19.RData")
# read orthology on Gencode v19
my.orthology <- read.table("2016-12-16_Correspondence_GeneName_Human_Mouse_Orthologs.txt",header=T,sep="\t")
my.orth.table <- unique(data.frame(cbind(my.orthology$Human_Symbol,my.orthology$Mouse_Symbol)))
colnames(my.orth.table) <- c("Human_Symbol","Mouse_Symbol")
load("Male_Female/2018-11-02 Cerebellum_GTEx_data_DEseq2_aging_genename_maleFemale.RData")
load("Male_Female/2018-11-02 Liver_GTEx_data_DEseq2_aging_genename_maleFemale.RData")
load("Male_Female/2018-11-03 Heart_GTEx_data_DEseq2_aging_genename_maleFemale.RData")
perform_tissue_comp("Liver_bothSex", my.liver.gtex.process, my.liver.RNAseq.process[[1]], my.orth.table)
