This is a script to evaluate the effect sizes of the 2 linear models.

Load in data

getwd()
[1] "/Users/laurenblake/Desktop/Regulatory_Evol/ashlar-trial/analysis"
# import sample labels
samples <- read.delim("../../../Reg_Evo_Primates/data/Sample_info_RNAseq_RIN.txt")
RIN <- samples$RIN
# expression
exprs <- read.table("../../../Reg_Evo_Primates/data/human_chimp_orth_exp_methyl_7725_hum.txt", sep="")
# methylation data
methyl <- read.csv("../../../Reg_Evo_Primates/data/chimp_human_orth_7725_avg_methyl_per_ts_gene.txt", sep="")

# Normalized gene expression data
cpm.voom.cyclic <- readRDS("../../../Reg_Evo_Primates/data/human_chimp_orth_cpm_voom_cyclic.rds")


# Load libraries
library("edgeR")
Loading required package: limma
library("limma")
library("plyr")
library("ashr")
library("cowplot")
Warning: package 'cowplot' was built under R version 3.4.4
Loading required package: ggplot2
Warning: package 'ggplot2' was built under R version 3.4.4

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library("vashr")
Loading required package: SQUAREM
Loading required package: qvalue
library("devtools")
Warning: package 'devtools' was built under R version 3.4.4
#devtools::install_github("jhsiao999/mediation/pkg")
library("medinome")

Set FDR level/svalue

FDR_level <- 0.05

Human-chimp heart

# Check parameters

human_chimp_heart <- c(1, 5, 9, 13, 20, 24, 28)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]



check_values <- mediate.test.regressing(Y, X, M, RIN_subset)
fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash_normal <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
summary(fit_ash_normal$result$svalue < 0.05)
   Mode   FALSE    TRUE 
logical    7688      37

Human versus chimpanzee heart- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.1141498

Human versus chimpanzee heart- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp kidney

# Check parameters

human_chimp_heart <- c(2, 6, 10, 14, 17, 21, 25, 29)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee kidney- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.1222571

Human versus chimpanzee kidney- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp liver

# Check parameters

human_chimp_heart <- c(3, 7, 11, 15, 18, 22, 26, 30)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee liver- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.2562814

Human versus chimpanzee liver- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp lung

# Check parameters

human_chimp_heart <- c(4, 8, 12, 16, 19, 23, 27, 31)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee lung- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.1155378

Human versus chimpanzee lung- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Rerun at FDR/FSR = 0.1

FDR_level <- 0.1

Human-chimp heart

# Check parameters

human_chimp_heart <- c(1, 5, 9, 13, 20, 24, 28)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee heart- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.06775244

Human versus chimpanzee heart- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp kidney

# Check parameters

human_chimp_heart <- c(2, 6, 10, 14, 17, 21, 25, 29)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee kidney- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.06144393

Human versus chimpanzee kidney- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp liver

# Check parameters

human_chimp_heart <- c(3, 7, 11, 15, 18, 22, 26, 30)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee liver- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.1764706

Human versus chimpanzee liver- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp lung

# Check parameters

human_chimp_heart <- c(4, 8, 12, 16, 19, 23, 27, 31)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee lung- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.06681034

Human versus chimpanzee lung- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Rerun at FDR/FSR = 0.01

FDR_level <- 0.01

Human-chimp heart

# Check parameters

human_chimp_heart <- c(1, 5, 9, 13, 20, 24, 28)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee heart- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)
fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.350365

Human versus chimpanzee heart- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:7]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=5, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.0005271481

Human-chimp kidney

# Check parameters

human_chimp_heart <- c(2, 6, 10, 14, 17, 21, 25, 29)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee kidney- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.1914894

Human versus chimpanzee kidney- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0

Human-chimp liver

# Check parameters

human_chimp_heart <- c(3, 7, 11, 15, 18, 22, 26, 30)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee liver- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.3898305

Human versus chimpanzee liver- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.0005217845

Human-chimp lung

# Check parameters

human_chimp_heart <- c(4, 8, 12, 16, 19, 23, 27, 31)

# Methylation
hc_exprs <- exprs[,2:48]
exprs_methyl <- exprs[,49:79]

# HC heart only


hc_exprs_heart <- hc_exprs[,human_chimp_heart]


methyl <- exprs_methyl[,human_chimp_heart]



Y <- hc_exprs_heart
two_species <- samples$Species[human_chimp_heart]
X <- droplevels.factor(two_species)
M <- methyl
RIN_subset <- RIN[human_chimp_heart]

Human versus chimpanzee liver- DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val < FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0.2631579

Human versus chimpanzee liver- non DE genes

# Run the linear model in limma
  design_1 <- model.matrix(~ X + RIN_subset)
  fit_all <- lmFit(cpm.voom.cyclic[,human_chimp_heart], design_1)
  fit_all <- eBayes(fit_all)

# Get results
  HvC_Heart_fit_all = topTable(fit_all, coef=2, adjust="BH", number=Inf, sort.by="none")
  HvC_Heart_fit_all_5perc <-
HvC_Heart_fit_all[which(HvC_Heart_fit_all$adj.P.Val > FDR_level), ]
  
 human_chimp_heart1 <-  rownames(methyl) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(methyl, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
M <- counts_human_chimp_heart_subset

human_chimp_heart1 <-  rownames(hc_exprs_heart) %in% rownames( HvC_Heart_fit_all_5perc)
human_chimp_heart1 <- as.data.frame(human_chimp_heart1)
counts_genes_in <- cbind(hc_exprs_heart, human_chimp_heart1)
counts_genes_in_cutoff <- subset(counts_genes_in, human_chimp_heart1 == "TRUE")
counts_human_chimp_heart_subset <- counts_genes_in_cutoff[,1:8]
Y <- counts_human_chimp_heart_subset

check_values <- mediate.test.regressing(Y, X, M, RIN_subset)

fit <- vash(check_values$d_se,df=6, singlecomp = T, unimodal = "auto") 
fit_ash <- ash(as.vector(check_values$d), fit$sd.post, mode = 0, mixcompdist = "normal")
Due to absence of package REBayes, switching to EM algorithm
calc <- as.array(summary(fit_ash$result$svalue < FDR_level))
calc2 <- as.numeric(calc[[2]])

(nrow(Y)-calc2)/nrow(Y)
[1] 0