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Codes_for_figures.Rmd
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---
title: "Codes for Figures"
author: "Zhe Fei"
date: "2023-06-12"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
```
## Prepare the data
```{r}
source("Codes_AROCM/0_fns_v2.R")
source("Codes_AROCM/1_prepare_data.R")
```
## Figure 1: Dog AROCM
```{r}
datN12all = read.csv("/Users/feiz/Dropbox/N12.2018-9238DogBloodOstrander/SampleSheetAgeN12final.csv")
datN12 = datN12all%>%
filter(ConfidenceInAgeEstimate>= 90 &
CanBeUsedForAgingStudies == "yes")
colnames(datN12)
length(unique(datN12$Breed.Index))
tabDogBreed = sort(table(datN12$DogBreedShort))
table(datN12$Age/datN12$LifespanMedianHorvath/1.5 >1)
table(datN12$Age/datN12$LifespanUpperHorvath >1)
View(datN12%>%filter(Age> 1.5*LifespanMedianHorvath))
range(datN12$Age/datN12$LifespanUpperHorvath)
range(datN12$Age/datN12$LifespanMedianHorvath)
# LifespanMedianHorvath
# LifespanUpperHorvath
# Weight.kg.avg
```
```{r}
m1 = match(datN12$Basename, colnames(dat0sesame))
summary(m1)
```
### fit slopes
```{r}
vnum = "dog_v5"
cex.main=1.5
cut1 = freq = 3
j=2
head(statesPRC2)
{
cgid = cg_list[[j]]
len1 = statesPRC2[j]
dogslopes = data.frame(
matrix(NA, nrow = length(tabDogBreed), ncol = 10)
)
for(nb in 1:length(tabDogBreed)){
idx = which(datN12$DogBreedShort == names(tabDogBreed)[nb])
dogslopes[nb,1] = names(tabDogBreed)[nb]
dogslopes[nb,2] = length(idx)
dogslopes[nb,3] = datN12$LifespanMedianHorvath[idx[1]]
dogslopes[nb,4] = datN12$LifespanUpperHorvath[idx[1]]
dogslopes[nb,5] = datN12$Weight.kg.avg[idx[1]]
basename = datN12$Basename[idx]
age1 = datN12$Age[idx]
m1 = match(basename, colnames(dat0sesame))
cgmean = colMeans(dat0sesame[cgid,m1])
dogslopes[nb,6:10] = fit1 = AROCM(cgmean, age1)
}
colnames(dogslopes) = c("DogBreed", "Freq", "LifespanMedianHorvath",
"LifespanUpperHorvath", "Weight.kg.avg",
names(fit1))
}
```
```{r}
dogslopes$SD_Rage =
dogslopes$SD_Age/dogslopes$LifespanMedianHorvath/2
dogslopes$Breed.Index =
datN12$Breed.Index[match(dogslopes$DogBreed, datN12$DogBreedShort)]
tmpd = datN12%>%filter(Breed.Index == 67)
tmpd = datN12%>%filter(Breed.Index == 11)
range(tmpd$Age/tmpd$LifespanMedianHorvath/2)
```
```{r}
colnames(dogslopes)
dogplot = dogslopes%>%filter(R2> 0.2 & AROCM < 0.5) ## R2> 0.2 &
ggplot(dogplot,
aes(x = LifespanMedianHorvath, y = AROCM,
label = Breed.Index)) +
geom_text()
```
- check outliers
```{r}
nb = 90
idx = which(datN12$Breed.Index == nb)
basename = datN12$Basename[idx]
age1 = datN12$Age[idx]
m1 = match(basename, colnames(dat0sesame))
cgmean = colMeans(dat0sesame[cgid,m1])
plot(age1, cgmean)
```
### Adjusted AROCM
- AROCM vs. SD_Rage
```{r}
dogplot = dogslopes%>%filter(R2> 0.2 & AROCM < 0.5) ## R2> 0.2 &
ggplot(dogplot,
aes(x = SD_Rage, y = AROCM,
label = Breed.Index)) +
geom_text()
```
```{r}
pvec = c(-20:20)*0.05
CV = QCOD = Cor_SD_Rage = NULL
for (p in pvec) {
adjROC = dogplot$AROCM * (dogplot$SD_Rage)^(1 - p)
adjCor = dogplot$Cor / (dogplot$SD_Rage)^(p)
CV = c(CV, sd(adjCor)/mean(adjCor))
QCOD = c(QCOD, qcod(adjCor) )
Cor_SD_Rage = c(Cor_SD_Rage, cor(adjCor, dogplot$SD_Rage) )
}
adjpmat = data.frame(
pvec,
CV,
QCOD,
Cor_SD_Rage
)
write.csv(adjpmat, paste0("DogSlopes/adjROC_select_power_",
len1,"_",vnum, ".csv"))
```
- Optimal p and Adj.ROC, adj.cor
```{r}
popt = 0.1
dogslopes$adjROC = dogslopes$AROCM * (dogslopes$SD_Rage)^(1 - popt)
dogslopes$adjCor = dogslopes$Cor / (dogslopes$SD_Rage)^(popt)
# dogplot
```
### Plot slopes
- AROCM vs. Lifespan on log scale
```{r}
colnames(dogslopes)
dogplot = dogslopes%>%filter(R2> 0.2 & AROCM < 0.5)
cor1 = with(dogplot, cor.test(log(LifespanMedianHorvath), log(AROCM)))
p1 = ggplot(dogplot,
aes(log(LifespanMedianHorvath), log(AROCM))) +
geom_point() + ggtitle(paste0("Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2)))
p1
```
#### AROCM vs. Lifespan
- regression:
```{r}
dogslopes$LifespanMedianScaled =
2 * dogslopes$LifespanMedianHorvath
colnames(dogslopes)
dogplot = dogslopes%>%filter(R2> 0.2 & AROCM < 0.5)
colnames(dogplot)
x = 1/dogplot$LifespanMedianScaled
y = dogplot$adjROC
lm0 = lm(y~x - 1)
summary(lm0)
plot(x,y)
```
#### AROCM vs. Lifespan
```{r}
cor1 = with(dogplot, cor.test(LifespanMedianScaled, AROCM))
p1_AROCM = ggplot(dogplot,
aes(LifespanMedianScaled, AROCM, label = Breed.Index) ) +
geom_text() + xlab("Lifespan") +
ggtitle(paste0("a. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2))) +
theme_bw()
p1_AROCM
```
#### AROCM vs. a/Lifespan
```{r}
cor1 = with(dogplot, cor.test(1/LifespanMedianScaled, AROCM))
x = 1/dogplot$LifespanMedianScaled
y = dogplot$AROCM
coef1 = coef(lm(y~x-1))
dogplot$X = coef1*x
xr = range(c(dogplot$X, dogplot$AROCM))
p1_AROCM_a = ggplot(dogplot,
aes(X, AROCM, label = Breed.Index) ) +
geom_text() + xlab(paste0("a/Lifespan, a=",round(coef1,2))) +
ggtitle(paste0("c. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2))) +
xlim(xr) + ylim(xr) +
geom_abline(intercept = 0,slope = 1, linetype=3) +
theme_bw()
p1_AROCM_a
```
#### Adj.AROCM vs. lifespan
```{r}
colnames(dogplot)
cor1 = with(dogplot, cor.test(LifespanMedianScaled, adjROC))
p1_adjroc = ggplot(dogplot,
aes(LifespanMedianScaled, adjROC, label = Breed.Index) ) +
geom_text() + xlab("Lifespan") + ylab("Adj.AROCM") +
ggtitle(paste0("b. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2))) +
theme_bw()
p1_adjroc
```
#### ADj. AROCM vs. a/Lifespan
```{r}
cor1 = with(dogplot, cor.test(1/LifespanMedianScaled, adjROC))
x = 1/dogplot$LifespanMedianScaled
y = dogplot$adjROC
coef1 = coef(lm(y~x-1))
dogplot$X = coef1*x
xr = range(c(dogplot$X, dogplot$adjROC))
p1_adjROC_a = ggplot(dogplot,
aes(X, adjROC, label = Breed.Index) ) +
geom_text() + ylab("Adj.AROCM") +
xlab(paste0("a/Lifespan, a=",round(coef1,2))) +
ggtitle(paste0("d. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2))) +
xlim(xr) + ylim(xr) +
geom_abline(intercept = 0,slope = 1, linetype=3) +
theme_bw()
p1_adjROC_a
```
#### AROCM vs. weight
```{r}
colnames(dogplot)
cor1 = with(dogplot, cor.test(Weight.kg.avg, AROCM))
p1_AROCM_wt = ggplot(dogplot,
aes(Weight.kg.avg, AROCM, label = Breed.Index) ) +
geom_text() + xlab("Weight") + ylab("AROCM") +
ggtitle(paste0("e. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2)))+
theme_bw()
p1_AROCM_wt
cor1 = with(dogplot, cor.test(Weight.kg.avg, adjROC))
p1_adjroc_wt = ggplot(dogplot,
aes(Weight.kg.avg, adjROC, label = Breed.Index) ) +
geom_text() + xlab("Weight") + ylab("Adj.AROCM") +
ggtitle(paste0("f. Cor=",round(cor1$estimate,2),
", p=",signif(cor1$p.value,2))) +
theme_bw()
p1_adjroc_wt
```
#### Dog main figure 1
```{r}
vnum = "v6"
pdf(paste0("DogSlopes/dogslopes_main_",len1,"_",vnum, ".pdf"),
onefile = TRUE,width = 6, height = 9)
grid.arrange(p1_AROCM,p1_adjroc,
p1_AROCM_a,p1_adjROC_a,
p1_AROCM_wt, p1_adjroc_wt,
nrow=3)
dev.off()
write.csv(dogslopes,
paste0("DogSlopes/dogslopes_",len1,"_",vnum, ".csv"))
dogBreedAROCM = dogplot%>%select(
DogBreed, AROCM, adjROC, LifespanMedianScaled
)
colnames(dogBreedAROCM)[3] = c("AdjAROCM")
write.csv(dogBreedAROCM, paste0("DogSlopes/dogBreedAROCM_",len1,"_",vnum, ".csv"))
```
## Figure 2: Dog Cor Bar Plot
### barplot Cor and Adj.Cor
```{r}
colnames(dogplot)
dogplot = dogplot[order(dogplot$LifespanMedianScaled),]
dogplot$rank = 1:nrow(dogplot)
dogplot$breedlab = paste0(dogplot$Breed.Index,". ",dogplot$DogBreed)
```
```{r}
cor.test(log(dogplot$LifespanMedianScaled), log(dogplot$Cor))
cor.test(log(dogplot$LifespanMedianScaled), log(dogplot$adjCor))
```
```{r}
pbar_cor = ggplot(dogplot) +
geom_bar(aes(y = Cor, x = rank),
stat="identity", position="dodge",
width = 1,
fill="grey", col="black",na.rm=TRUE) +
coord_flip() + theme_bw() +
geom_hline(yintercept = mean(dogplot$Cor), col="red") +
labs(y = "Cor(Age,Methyl)", title = "") +
scale_x_continuous("Breed", # limits = range(dogplot$rank),
breaks = dogplot$rank,
labels = dogplot$breedlab) +
# scale_y_continuous(expand = c(0,0)) +
theme(axis.text=element_text(size=8),
axis.ticks.x = element_blank(),
plot.title = element_text(size = 11, hjust = 0.5)) +
theme(legend.position = "none")
```
```{r}
pbar_adjcor = ggplot(dogplot) +
geom_bar(aes(y = adjCor, x = rank),
stat="identity", position="dodge",
width = 1,
fill="grey", col="black",na.rm=TRUE) +
coord_flip() + theme_bw() +
geom_hline(yintercept = mean(dogplot$adjCor), col="red") +
labs(y = "Adj.Cor(Age,Methyl)", title = "") +
theme(axis.text=element_text(size=8),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 11, hjust = 0.5)) +
theme(legend.position = "none")
pbar_adjcor
```
```{r}
pdf(paste0("DogSlopes/BarplotCor_",vnum,".pdf"),
width = 7, height = 10)
hlay = matrix(c(1,1,2,
1,1,2,
1,1,2),
nrow = 3 ,byrow = TRUE)
grid.arrange(pbar_cor, pbar_adjcor,
nrow=1,layout_matrix=hlay)
dev.off()
```
## Figure 3 & 4: All chromatin states and single species plots
```{r}
############ 1. summary state Slopes
stateSlopes = statesTab
dim(stateSlopes)
head(stateSlopes)
# stateSlopes$Cor.Slope.Identity=NULL
length(statesPRC2)
```
- Different correlations for each state
```{r}
scaleCpG
colnames(SpeciesMat)[9:11]
if(scaleCpG){
idx1 = which(SpeciesMat$RemoveFei==0)
nc = c(10:12, grep("Slope",colnames(SpeciesMat)))
speciescor = combineTissues(SpeciesMat,idx1, nc)
name1 = "IdentityScaled"
}
dim(speciescor)
head(colnames(speciescor))
# name1
# idx1 = which(SpeciesMat$RemoveFei==0)
colnames(stateSlopes)
stateSlopes$CorSpearman = stateSlopes$CorLog =
stateSlopes$`Cor1/Lifespan` =
stateSlopes$`Cor1/Slope` = stateSlopes$NumberPositiveSlopes = NA
```
- correlations of each state
```{r}
pdf(paste0("../",outfolder,"/SpeciesStates_corplot.pdf"),
onefile = TRUE)
par(mfrow=c(2,2))
ncut = 60
for(k in 1:length(statesPRC2)){
us = statesPRC2[k]
nc = grep(us, colnames(speciescor), fixed = TRUE)
slope1 = speciescor[,nc]
slope1 = slope1[,11] ### 10 or 11
col1 = grep(paste0(us,"OldSlope1"), colnames(SpeciesMat),
fixed = TRUE)[1]
tmpslope = SpeciesMat[SpeciesMat$RemoveFei==0,col1]
tmp1 = which(stateSlopes$statesPRC2 == us)
# print(tmp1)
x = speciescor$maxAgeCaesar
stateSlopes$NumberPositiveSlopes[tmp1] = npos = sum(tmpslope > 0 & !is.na(tmpslope))
# slope2 = slope1 - min(slope1,na.rm = T) + 1
if(npos < ncut ) {
slope2 = slope1
slope2[slope1>0] = NA
}
stateSlopes$CorLog[tmp1] =
ifelse(npos<ncut, cor(-log(x), log(-slope2),
use = "pairwise.complete"),
cor(log(x), log(slope1), use = "pairwise.complete") )
stateSlopes$CorSpearman[tmp1] =
cor(x, slope1, use = "pairwise.complete", method = "spearman")
stateSlopes$`Cor1/Lifespan`[tmp1] =
cor(1/x, slope1, use = "pairwise.complete")
#slope2 = slope1 - 1.5*min(slope1,na.rm = T)
stateSlopes$`Cor1/Slope`[tmp1] = ifelse(npos<ncut,
cor(x, 1/slope2, use = "pairwise.complete"),
cor(x[slope1>0], 1/slope1[slope1>0], use = "pairwise.complete"))
# cor(x[slope1>0], 1/slope1[slope1>0], use = "pairwise.complete")
main1 = paste0(us,"\n")
xlab = "maxAge"
ylab = "Slope"
verboseScatterplot(x, slope1, main = main1,
xlab=xlab,ylab=ylab)
verboseScatterplot(log(x), log(slope1), main = main1,
xlab=paste0("log ",xlab),ylab=paste0("log ",ylab))
slope3 = slope1
slope3[slope1<0] = NA
if(npos<ncut) y = 1/slope2 else y = 1/slope3
verboseScatterplot(x, y, main = main1,
xlab=xlab,ylab=paste0("1/",ylab))
verboseScatterplot(1/x, slope1, main = main1,
xlab=paste0("1/",xlab),ylab=ylab)
}
dev.off()
```
- all states combined
```{r}
# View(stateSlopes)
colnames(stateSlopes)
nc = 8
# stateSlopes[,nc] = -stateSlopes[,nc]
stateSlopes = stateSlopes[order(stateSlopes[,nc]),]
rbPal <- colorRampPalette(c('blue','red'))
rbPal(2)
#This adds a column of color values
# based on the y values
stateSlopes$Col <- rbPal(nrow(stateSlopes))
y = - stateSlopes[,nc]
head(sort(y),12)
tail(sort(y),12)
corl = -0.36
coru = 0.539
idx1 = which(y< corl |
(y> coru & stateSlopes$NumberPositiveSlopes>100))
idx1 = which(y< corl | (y> coru))
# head(stateSlopes$statesPRC2)
n1 = 6
n2 = 4
idx1 = c(1:n1, tail(1:nrow(stateSlopes),n2))
stateSlopes$statesPRC2[idx1]
write.csv(stateSlopes, paste0("../",outfolder,"/stateSlopes_",name1, ".csv"), row.names = 1:nrow(stateSlopes))
pdf(paste0("../",outfolder,"/stateSlopes_",name1, ".pdf"))
y = stateSlopes$Freq
x = -stateSlopes[,nc]
{
plot(x,y, pch=19, col = stateSlopes$Col, xlim=c(-1,1),
ylab = "# of CpGs", xlab = "Cor(AROCM, 1/LifeSpan)",
main = "CpG lists Correlations with lifespan")
text(x[idx1],y[idx1], labels = stateSlopes$statesPRC2[idx1],
pos=c(rep(4, n1),
rep(2, n2)),
cex=0.75)
abline(v= corl, col="blue", lty= 2)
abline(v= coru, col="red", lty= 2)
}
dev.off()
```
### Prepare Figure 1
```{r}
# View(SpeciesMat[,1:16]%>%filter(Tissue == "Blood"))
specs = SpeciesMat$MammalNumberHorvath[SpeciesMat$Tissue == "Blood" &
SpeciesMat$Freq>80]
## human skin
j=2
len1 = statesPRC2[j]
cgid = cg_list[[j]]
specs = c("9.2", "9.1", "4.25", "4.26", "5.12", "1.4","5.25","6.4", "6.2",
"3.23", "3.22", "3.21", "2.2","2.1",
"3.14", "1.1", "3.11")
specs = c("4.11.1", "5.1.1", "6.2.1", "1.4.1", "4.19.1", "2.1.1", "6.1.1", "1.1.1",
"2.1.2", "4.12.3", "9.9.1", "4.13.7", "5.9.1", "9.9.3", "4.7.1", "4.7.2", "4.13.8")
specs = c("Rattus norvegicus", "Mus musculus",
"Sus scrofa domesticus","Sus scrofa minusculus",
"Canis lupus familiaris", "Chlorocebus aethiops sabaeus",
"Ceratotherium simum simum", "Phoca vitulina",
"Delphinapterus leucas", "Equus caballus",
"Loxodonta africana", "Tursiops truncatus",
"Orcinus orca", "Elephas maximus",
"Megaptera novaeangliae", "Homo sapiens",
"Balaena mysticetus")
tiss = c(rep("Blood", 14),
rep("Skin", 3))
table(SpeciesMat$Tissue=="Skin")
specs = SpeciesMat$MammalNumberHorvath[SpeciesMat$Tissue == "Skin"]
tiss = rep("Skin", length(specs))
```
- Final Figure 1
```{r}
stateSlopes = read.csv("/Users/feiz/Documents/Box Sync/Horvath/Mammalian meth projects/Slope/MainFigures/stateSlopes_IdentityScaled.csv",
row.names = 1)
vnum = "v8"
caseYO = 3 ## 1: Young - 0.1L, Old - ASM; 2: 0.1L; 3: ASM
# matratio = SpeciesMat$AvgMaturity/SpeciesMat$maxAgeCaesar
matratio = 0.1
statesfolder = "out_AROCM_0.1L"
```
```{r}
j=2
len1 = statesPRC2[j]
cgid = cg_list[[j]]
colnames(stateSlopes)
nc = 8
#pdf(paste0(outfolder,"/Figure1_",vnum, ".pdf"))
cex = 1.4
cex.main = cex.lab = 2
y = stateSlopes$Freq
x = -stateSlopes[,nc]
idx1 = which(stateSlopes[,nc]< corl |
stateSlopes[,nc]> coru)
idx1 = c(1:n1, tail(1:nrow(stateSlopes),n2))
stateSlopes$statelabel[idx1] = stateSlopes$statesPRC2[idx1]
range(stateSlopes$CorSpearman)
fig1a = ggplot(stateSlopes,aes(-CorSpearman,Freq,col=Col,label = statelabel))+
geom_point() + geom_text(hjust=0, vjust=0, size = 3) +
xlim(-0.5, 1.1) +
ylab("# of CpGs") + xlab("Spearman Cor(AROCM, 1/Lifespan)") +
ggtitle("a. Corr in 54 chromatin states")+
geom_vline(aes(xintercept=corl), colour="blue", linetype="dashed") +
geom_vline(aes(xintercept=coru), colour="red", linetype="dashed")+
theme_bw() + theme(legend.position = "")
write.csv(stateSlopes,
paste0("../",outfolder,"/stateSlopes_",vnum, ".csv"),
row.names = 1:nrow(stateSlopes))
```
### Figure 1b and 1c
```{r}
with(dat_scaledM_rage,cor(Rage, ScaledM))
rmcorr(Spec,Rage, ScaledM, dataset = dat_scaledM_rage)
cors2 = plyr::ddply(dat_scaledM_rage, "Spec",
function(mat1){
data.frame(
cor_rage = cor(mat1$Rage, mat1$ScaledM),
cor_tage = cor(mat1$tage, mat1$ScaledM),
cor_logliage = cor(mat1$logliage, mat1$ScaledM)
)
})
summary(cors2)
```
```{r}
lm_age = lm(ScaledM~ logliage, data = dat_scaledM_rage)
coef(lm_age)
p_logliage = ggplot(dat_scaledM_rage,aes(x=logliage, y = ScaledM,color=Spec)) +
# geom_text(aes(color=Spec, label = SpecNum),hjust=0, vjust=0, size = 1) +
geom_point() +
geom_smooth(method = lm,se = FALSE,linewidth=0.3,linetype=2)+
geom_abline(slope = coef(lm_age)[2], intercept = coef(lm_age)[1]) +
xlab("Logli RAge") + ggtitle("c. Median Cor=0.76") +
theme_bw() + theme(legend.position = "",axis.title.y=element_blank())
p_logli = ggplot(dat_scaledM_rage,aes(x=logliage, y = ScaledM,color=Spec)) +
geom_text(aes(color=Spec, label = SpecNum),hjust=0, vjust=0, size = 1) +
# geom_point() +
geom_smooth(method = lm,se = FALSE,linewidth=0.3,linetype=2)+
geom_abline(slope = coef(lm_age)[2], intercept = coef(lm_age)[1]) +
xlab("Logli Relative Age (N=10932)") + ggtitle("c. Median Cor=0.76, p=1e-16") +
theme_bw() + theme(legend.position = "",axis.title.y=element_blank())
lm_Rage = lm(ScaledM~Rage, data = dat_scaledM_rage)
coef(lm_Rage)
lfit = loess(ScaledM ~ Rage, data = dat_scaledM_rage)
dat_scaledM_rage$loessfit = lfit$fitted
p_rage = ggplot(dat_scaledM_rage,aes(x=Rage, y = ScaledM)) +
geom_point(aes(color=Spec)) +
## geom_text(aes(color=Spec, label = SpecNum),hjust=0, vjust=0, size = 1) +
geom_smooth(aes(x=Rage, y = ScaledM), data = dat_scaledM_rage,
formula = y~logli(x,m1=0.1),se=FALSE,linewidth=0.5,col="black") +
# geom_abline(slope = coef(lm_Rage)[2], intercept = coef(lm_Rage)[1]) +
xlab("Relative Age") + ggtitle("b. Median Cor=0.73") + theme_bw() +
theme(legend.position = "")
p_ra = ggplot(dat_scaledM_rage,aes(x=Rage, y = ScaledM)) +
## geom_point(aes(color=Spec)) +
geom_text(aes(color=Spec, label = SpecNum),hjust=0, vjust=0, size = 1) +
geom_smooth(aes(x=Rage, y = ScaledM), data = dat_scaledM_rage,
formula = y~log(x),se=FALSE,linewidth=0.5,col="black") +
# geom_abline(slope = coef(lm_Rage)[2], intercept = coef(lm_Rage)[1]) +
xlab("Relative Age (N=10932)") + ggtitle("b. Median Cor=0.73, p=1e-16") + theme_bw() +
theme(legend.position = "")
```
```{r, eval=FALSE}
with(dat_scaledM_rage,cor(tage, ScaledM))
rmcorr(Spec, tage, ScaledM, dataset = dat_scaledM_rage)
lm_tage = lm(ScaledM~tage, data = dat_scaledM_rage)
coef(lm_tage)
p_tage = ggplot(dat_scaledM_rage,aes(x=tage, y = ScaledM,
color=Spec, label = SpecNum)) +
## geom_point(size=0.5, alpha=0.5) +
geom_text(hjust=0, vjust=0, size = 1) +
geom_smooth(method = lm,se = FALSE,linewidth=0.3,linetype=2)+
geom_abline(slope = coef(lm_tage)[2], intercept = coef(lm_tage)[1]) +
xlab("Log-Log Relative Age") + ggtitle("b. Median Cor=0.74") + theme_bw() +
theme(legend.position = "")
colnames(dat_scaledM_rage)
```
### Single species
```{r}
dim(tslopes)
tslopes = as.data.frame(tslopes)
tslopes$Cor_diff = tslopes$Cor_TAge - tslopes$Cor_Age
```
```{r}
ss = c("9.9.1", "6.1.1", "1.1.1",
"12.3.10", "9.9.1", "9.1.3",
"1.4.1", "4.7.1", "4.19.1")
# another mouse tissue
## 4.19.1. 6.2.1 4.13.4. 9.1.3(Skin) 1.4.1. 4.7.1 (blood, kidney) 4.7.2
sname = c("Mouse","Horse","Human",
"Shrew","Mouse","Naked mole-rat",
"Green monkey","Pig", "Beluga whale")
tissues = c("Blood", "Blood", "Skin",
"Tail","Cerebellum","Skin",
"Cortex","Blood", "Blood")
data.frame(
ss,sname,tissues
)
```
```{r}
ss_age = list()
for (nk in 1:length(ss)) {
ylab1 = ifelse(nk==1,"ScaledM","")
idxss = with(dat_scaledM_rage, which(SpecNum == ss[nk]& Tissue == tissues[nk]))
cor1 = round(cor(dat_scaledM_rage$ScaledM[idxss], dat_scaledM_rage$Age[idxss]),2)
tit1 = paste0(letters[nk+ceiling(nk/3)*3],". ",sname[nk],"_",tissues[nk],", Cor=",cor1)
xint = 0.1*dat_scaledM_rage$Lifepspan[idxss[1]]
datplot = dat_scaledM_rage[idxss,]
# ymin = min(datplot$MeanMethyl)
# ymax = max(datplot$MeanMethyl)
ymin = min(datplot$ScaledM)
ymax = max(datplot$ScaledM)
maxage = dat_scaledM_rage$Lifepspan[idxss[1]]
maturity = dat_scaledM_rage$ASM[idxss[1]]
lm1 = lm(ScaledM ~ Age, data = datplot[datplot$Age<= xint,])
xr1 = range(datplot[datplot$Age<= xint,]$Age)
yr1 = range(lm1$fitted.values)
slopey = coef(lm1)[2]
lm2 = lm(ScaledM ~ Age, data = datplot[datplot$Age> xint,])
xr2 = range(datplot[datplot$Age> xint,]$Age)
yr2 = range(lm2$fitted.values)
slopeo = coef(lm2)[2]
lm3 = lm(ScaledM ~ Age, data = datplot)
xr3 = range(datplot$Age)
yr3 = range(lm3$fitted.values)
slopea = coef(lm3)[2]
segdat = data.frame(
x = c(xr1[1], xr2[1], xr3[1]), ## 0, xint, 0,
y = c( yr1[1], yr2[1], yr3[1]), ## ymin*0.92, ymin*0.91, ymin*0.9,
xend = c( xr1[2], xr2[2], xr3[2]), ## xint, 10*xint, 10*xint,
yend = c( yr1[2], yr2[2], yr3[2]), ## ymin*0.92, ymin*0.91, ymin*0.9,
col = c("blue","red","black"),
slopex = c(mean(xr1), mean(xr2), mean(xr3)),
slopey = c(yr1[1], yr2[1], yr3[2]),
slopet = round(c(slopey,slopeo,slopea),2)
)
ss_age[[nk]] = ggplot(datplot,
aes(x=Age, y = ScaledM)) +
geom_point(size=1, alpha=0.5) + geom_vline(xintercept = xint,linetype=2)+
## geom_text(hjust=0, vjust=0, size = 1) +
# geom_smooth(method = lm,se = FALSE,linewidth=0.5,linetype=2)+
xlab("Age") + ylab(ylab1) + ggtitle(tit1) + # ylim(ymin, ymax) +
geom_segment(aes(x=x,y=y,xend=xend,yend=yend,col=col),data=segdat) +
geom_text(aes(x=slopex, y=slopey, label = slopet, col=col), # color=col,
# label.size = 0, fill=NA,
fontface = "bold",
data=segdat) +
theme_bw() + theme(legend.position = "",plot.title = element_text(size = 10))
}
ss_age[[nk]]
```
- last 3 species as panels d,e,f
```{r}
for (nk in 7:9 ) {
ylab1 = ifelse(nk==7,"ScaledM","")
idxss = with(dat_scaledM_rage, which(SpecNum == ss[nk]& Tissue == tissues[nk]))
cor1test = cor.test(dat_scaledM_rage$ScaledM[idxss], dat_scaledM_rage$Age[idxss])
cor1 = round(cor1test$estimate,2)
pv1 = signif(cor1test$p.value,1)
tit1 = paste0(letters[nk-3],". ",
sname[nk],"_",tissues[nk],
", Cor=",cor1,", p=",pv1)
xint = 0.1*dat_scaledM_rage$Lifepspan[idxss[1]]
datplot = dat_scaledM_rage[idxss,]
# ymin = min(datplot$MeanMethyl)
# ymax = max(datplot$MeanMethyl)
ymin = min(datplot$ScaledM)
ymax = max(datplot$ScaledM)
maxage = dat_scaledM_rage$Lifepspan[idxss[1]]
maturity = dat_scaledM_rage$ASM[idxss[1]]
lm1 = lm(ScaledM ~ Age, data = datplot[datplot$Age<= xint,])
xr1 = range(datplot[datplot$Age<= xint,]$Age)
yr1 = range(lm1$fitted.values)
slopey = coef(lm1)[2]
lm2 = lm(ScaledM ~ Age, data = datplot[datplot$Age> xint,])
xr2 = range(datplot[datplot$Age> xint,]$Age)
yr2 = range(lm2$fitted.values)
slopeo = coef(lm2)[2]
lm3 = lm(ScaledM ~ Age, data = datplot)
xr3 = range(datplot$Age)
yr3 = range(lm3$fitted.values)
slopea = coef(lm3)[2]
segdat = data.frame(
x = c(xr1[1], xr2[1], xr3[1]), ## 0, xint, 0,
y = c( yr1[1], yr2[1], yr3[1]), ## ymin*0.92, ymin*0.91, ymin*0.9,
xend = c( xr1[2], xr2[2], xr3[2]), ## xint, 10*xint, 10*xint,
yend = c( yr1[2], yr2[2], yr3[2]), ## ymin*0.92, ymin*0.91, ymin*0.9,
col = c("blue","red","black"),
slopex = c(mean(xr1), mean(xr2), mean(xr3)),
slopey = c(yr1[1], yr2[1], yr3[2]),
slopet = round(c(slopey,slopeo,slopea),2)
)
ss_age[[nk]] = ggplot(datplot,
aes(x=Age, y = ScaledM)) +
geom_point(size=1, alpha=0.5) + geom_vline(xintercept = xint,linetype=2)+
## geom_text(hjust=0, vjust=0, size = 1) +
# geom_smooth(method = lm,se = FALSE,linewidth=0.5,linetype=2)+
xlab(paste0("Age (N=",length(idxss),")" )) + ylab(ylab1) + ggtitle(tit1) + # ylim(ymin, ymax) +
geom_segment(aes(x=x,y=y,xend=xend,yend=yend,col=col),data=segdat) +
geom_text(aes(x=slopex, y=slopey, label = slopet, col=col), # color=col,
# label.size = 0, fill=NA,
fontface = "bold",
data=segdat) +
theme_bw() + theme(legend.position = "",plot.title = element_text(size = 10))
}
```
```{r}
vnum = "v13"
pdf(paste0("../",outfolder,"/Fig1_",vnum,".pdf"),
width = 10.5,height = 7)
grid.arrange(fig1a, p_ra,p_logli,
ss_age[[7]],ss_age[[8]],ss_age[[9]],
# sslist[[1]],sslist[[2]],sslist[[3]],
nrow=2)
dev.off()
```
#### ss_age: relative age and logliage
```{r}
nkk = c(1:3,1:3,7:9)
for (nk in 4:length(ss)) {
ylab1 = ifelse(nk%%3==1,"ScaledM","")
idxss = with(dat_scaledM_rage, which(SpecNum == ss[nk]& Tissue == tissues[nk]))
cor1test = cor.test(dat_scaledM_rage$ScaledM[idxss], dat_scaledM_rage$Age[idxss])
cor1 = round(cor1test$estimate,2)
pv1 = signif(cor1test$p.value,1)
tit1 = paste0(letters[nkk[nk]],". ",sname[nk],"_",tissues[nk],
", Cor=",cor1, ", p=",pv1)
xint = 0.1
datplot = dat_scaledM_rage[idxss,]
# ymin = min(datplot$MeanMethyl)
# ymax = max(datplot$MeanMethyl)
ymin = min(datplot$ScaledM)
ymax = max(datplot$ScaledM)
maxage = dat_scaledM_rage$Lifepspan[idxss[1]]
maturity = dat_scaledM_rage$ASM[idxss[1]]
lm0 = lm(ScaledM ~ logliage, data = datplot)
datplot$fitted_logli = lm0$fitted.values
lm3 = lm(ScaledM ~ Rage, data = datplot)
datplot$fitted_rage = lm3$fitted.values
segdat = data.frame(
x = c(0, xint, 0),
y = c(ymin*0.92, ymin*0.91, ymin*0.9),
xend = c(xint, 10*xint, 10*xint),
yend = c(ymin*0.92, ymin*0.91, ymin*0.9),
col = c("blue","red","black"),
slopex = c(0.1, 0.7, 0.5),
slopey = c(ymin*0.97, ymin*0.95, ymin*0.85),
slopet = round(c(slopey,slopeo,slopea),2)
)
ss_age[[nk]] = ggplot(datplot,
aes(x=Rage, y = ScaledM)) +
geom_point(size=1, alpha=0.5) + geom_vline(xintercept = xint,linetype=2)+
## geom_text(hjust=0, vjust=0, size = 1) +
# geom_smooth(method = lm,se = FALSE,linewidth=0.5,linetype=2)+
geom_line(aes(x = Rage, y = fitted_rage), # se = FALSE,
linewidth=0.5,linetype=2,col="red") +
geom_smooth(aes(x = Rage, y = fitted_logli), se = FALSE,
linewidth=0.5,linetype=2,col="blue") +
xlab(paste0("Relative Age (N=",length(idxss),")" )) +
ylab(ylab1) + ggtitle(tit1) + # ylim(ymin, ymax) +
# geom_segment(aes(x=x,y=y,xend=xend,yend=yend,col=col),data=segdat) +
# geom_label(aes(x=slopex, y=slopey, col=col, label = slopet),data=segdat,
# label.size = NA, fill=NA) +
theme_bw() + theme(legend.position = "",plot.title = element_text(size = 10))
}
ss_age[[nk]]
```
#### sslist
```{r}
sslist = list()
nkk = c(1:6, 10:12)
for (nk in 1:length(ss)) {
ylab1 = ifelse(nk%%3==1,"ScaledM","")
idxss = with(dat_scaledM_rage, which(SpecNum == ss[nk]& Tissue == tissues[nk]))
cor1test = cor.test(dat_scaledM_rage$ScaledM[idxss], dat_scaledM_rage$Age[idxss])
cor1 = round(cor1test$estimate,2)
pv1 = signif(cor1test$p.value,1)
tit1 = paste0(letters[nkk[nk]],". ",sname[nk],"_",tissues[nk],
", Cor=",cor1, ", p=",pv1)
datplot = dat_scaledM_rage%>%
filter(SpecNum == ss[nk]&Tissue == tissues[nk])
lm0 = lm(ScaledM ~ logliage, data = datplot)
gamma1 = round(coef(lm0)[2],2)
pv2 = signif(summary(lm0)$coefficients[2,4],1)
sslist[[nk]] = ggplot(datplot,
aes(x=logliage, y = ScaledM)) +
geom_point(size=1, alpha=0.5) +
## geom_text(hjust=0, vjust=0, size = 1) +
geom_smooth(method = lm,se = FALSE,linewidth=0.5,linetype=2)+
# geom_abline(slope = coef(lm_tage)[2], intercept = coef(lm_tage)[1]) +
xlab(paste0("Logli Relative Age (N=",length(idxss),")")) +
ylab(ylab1) +
theme_bw() +
labs(title = tit1,
subtitle = bquote(gamma[1] ~ "=" ~ .(gamma1) ~ ", p="~ .(pv2)) ) +
theme(plot.title=element_text(size = titlesize),
legend.position = "",
plot.subtitle = element_text(size = 1.5*titlesize,
margin=margin(b=-20)))
}
sslist[[nk]]
```
#### Fig1_ctd
```{r}
pdf(paste0("../",outfolder,"/Fig1_ctd_v4.pdf"),
width = 9.6,height = 12)
grid.arrange(ss_age[[4]],ss_age[[5]],ss_age[[6]],
sslist[[4]],sslist[[5]],sslist[[6]],
ss_age[[7]],ss_age[[8]],ss_age[[9]],
sslist[[7]],sslist[[8]],sslist[[9]],
nrow=4)
dev.off()
```
```{r}
pdf(paste0("../",outfolder,"/SingleSpecies_1.pdf"),
width = 9,height = 6)
grid.arrange(ss_age[[1]],ss_age[[2]],ss_age[[3]],
sslist[[1]],sslist[[2]],sslist[[3]],