01.2_plot_PCs

#use this as a template to plot population PCs with and without HapMap merged
hapmappopinfo <- read.table("/home/lauren/MESA_dbGaP_55081/all_mesa_merged/hapmap_hg19/pop_HM3_hg19_forPCA.txt") %>% select(V1,V3)
colnames(hapmappopinfo) <- c("pop","IID")
fam_h<- read.table('/home/lauren/MESA_dbGaP_55081/all_mesa_merged/MESA_HIS_merged_hapmap.4pca.fam') %>% select(V1,V2)
colnames(fam_h) <- c("FID","IID")
popinfo <- left_join(fam_h,hapmappopinfo,by="IID")
popinfo <- mutate(popinfo, pop=ifelse(grepl('T',IID),'Norway',as.character(pop))) %>% mutate(pop=ifelse(is.na(pop),'HIS',as.character(pop)))
table(popinfo$pop)

pcs_hishap <- read.table("/home/lauren/MESA_dbGaP_55081/all_mesa_merged/his_merged_auto.evec",skip=1)
pcdf_hishap <- data.frame(popinfo,pcs_hishap[,2:11]) %>% rename(PC1=V2,PC2=V3,PC3=V4,PC4=V5,PC5=V6,PC6=V7,PC7=V8,PC8=V9,PC9=V10,PC10=V11)

gwas_hishap <- filter(pcdf_hishap,pop=='HIS') %>% mutate(PATNO=IID)
hm3 <- filter(pcdf_hishap,grepl('NA',IID))
table(gwas_hishap$pop)
table(hm3$pop)


##calc proportion variance explained by each PC
eval_hishap <- scan("/home/lauren/MESA_dbGaP_55081/all_mesa_merged/his_merged_auto.eval")[1:10]
round(eval_hishap/sum(eval_hishap),3)
#pdf(file=my.dir %&% "MESA_PCA.pdf")
ggplot() + geom_point(data=pcdf_hishap,aes(x=PC1,y=PC2,col=pop,shape=pop))+geom_point(data=hm3,aes(x=PC1,y=PC2,col=pop,shape=pop))+ theme_bw() + scale_colour_brewer(palette="Set1")

ggplot() + geom_point(data=gwas,aes(x=PC1,y=PC3,col=pop,shape=pop))+geom_point(data=hm3,aes(x=PC1,y=PC3,col=pop,shape=pop))+ theme_bw() + scale_colour_brewer(palette="Set1")

ggplot() + geom_point(data=gwas,aes(x=PC2,y=PC3,col=pop,shape=pop))+geom_point(data=hm3,aes(x=PC2,y=PC3,col=pop,shape=pop))+ theme_bw() + scale_colour_brewer(palette="Set1")
#dev.off()