load(file="AncestMat.Rda")
load(file="SubPopCorMat.Rda")
load(file="DenisovaMat.Rda")
load(file="NeanderMat.Rda")
load(file="PopulationMat.Rda")

colSums(DenisovaMat)

source("count_snvs.R")

length(res)


data <- read.table("KPGP1000_chr1_annot.txt",skip=2)

a1 <- which(data[[10]]<0.05)

length(a1)

a2 <- which(data[[10]]>(1/(2*1131-1)))

a0 <- intersect(a1,a2)
length(a0)

summary(data[[10]][a1])

summary(data[[10]][a0])

a3 <- which(data[[10]]>(2/(2*1131-1)))

a01 <- intersect(a1,a3)
length(a01)



#----------------

mean(data[[10]])

length(data[[10]])

aT <- table(data[[10]])


sum(aT[1:114])+ sum(aT[(2263-114+1):2263])

Rare SNPs (0<=MAF<=0.05 OR 0.95<=MAF<=1.0):

 
Without zero and privat:

sum(aT[3:114])+ sum(aT[(2263-114+1):(2263-2)])






freqs <- names(aT)

dim(aT) <- c(1,2264)

colnamesAT <- 0:2263

colnames(aT) <- colnamesAT

write.table(aT,file="MAFstatistics.txt")

s <- 0
for (i in 3:113) {
 s <- s + aT[i]*(i-1)
}

s


for (i in ((2263-113+1):(2263-2))) {
 s <- s + aT[i]*(2263-i)
}

s



s <- 0
for (i in 2:2263) {
 s <- s + aT[i]*(i-1)
}

s

3167651


MAF without 0 and privat SNPs and SNPs >0.05 MAF:
30994086/(2184*3201157)

[1] 0.004433219

MAF without 0 and privat SNPs and SNPs >0.05 MAF and with change of major:
31978538/(2184*3201157)
[1] 0.00457403

486510822/(2184*3201157)
[1] 0.06958777

##########################
RESULTS:


MAF: 
0.06958777

MAF without 0 and privat SNPs and SNPs >0.05 MAF:
0.004433219

MAF without 0 and privat SNPs and SNPs >0.05 MAF and with change of major:
0.00457403


3201157 SNPS
13665 (0.43%) SNPs with frequency 0

486510822 minor alleles 
30994086 minor alleles without 0 and private SNPs and >0.05 MAF
683851 (21.4%) private SNPs