# R/calc_BD.R In pievos101/PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses

```calc_BD <- function(bial, populations, outgroup, keep.site.info=FALSE, dxy.table=FALSE){

# Patterson's D
# f

# pop 1: populations[[1]] :
# pop 2: populations[[2]] :
# pop 3: populations[[3]] : archaic population

if(length(populations)!=3){
stop("This statistic requires 3 populations; the third population is the archaic population")
}

if(outgroup[1]==FALSE || length(outgroup[1])==0){
stop("This statistic needs an outgroup ! (set.outgroup)")
}

# calculate frequencies of the derived alleles.
freqs  <- jointfreqdist(bial,populations,outgroup,keep.all.sites=TRUE)
freqs  <- freqs\$jfd
#print(freqs)

# calc the B_d
p      <- freqs[1,]
q      <- freqs[2,]

# dxy for each site and each pop vs archaic pop 3

dxy_pop13 <- apply(bial,2,function(x){
pop1 <- x[populations[[1]]]
pop3 <- x[populations[[3]]]
ones <- sum(pop1==1,na.rm=TRUE) * sum(pop3==0,na.rm=TRUE)
zero <- sum(pop1==0,na.rm=TRUE) * sum(pop3==1,na.rm=TRUE)
dxy  <- (ones + zero)/(sum(!is.na(pop1))*sum(!is.na(pop3)))
return(dxy)
})

dxy_pop23 <- apply(bial,2,function(x){
pop2 <- x[populations[[2]]]
pop3 <- x[populations[[3]]]
ones <- sum(pop2==1,na.rm=TRUE) * sum(pop3==0,na.rm=TRUE)
zero <- sum(pop2==0,na.rm=TRUE) * sum(pop3==1,na.rm=TRUE)
dxy  <- (ones + zero)/(sum(!is.na(pop2))*sum(!is.na(pop3)))
return(dxy)
})

dxy_pop12 <- apply(bial,2,function(x){
pop1 <- x[populations[[1]]]
pop2 <- x[populations[[2]]]
ones <- sum(pop1==1,na.rm=TRUE) * sum(pop2==0,na.rm=TRUE)
zero <- sum(pop1==0,na.rm=TRUE) * sum(pop2==1,na.rm=TRUE)
dxy  <- (ones + zero)/(sum(!is.na(pop1))*sum(!is.na(pop2)))
return(dxy)
})

dxy_pop123 <- apply(bial,2,function(x){
pop1 <- x[c(populations[[1]],populations[[2]])]
pop2 <- x[populations[[3]]]
ones <- sum(pop1==1,na.rm=TRUE) * sum(pop2==0,na.rm=TRUE)
zero <- sum(pop1==0,na.rm=TRUE) * sum(pop2==1,na.rm=TRUE)
dxy  <- (ones + zero)/(sum(!is.na(pop1))*sum(!is.na(pop2)))
return(dxy)
})

if(dxy.table[[1]]!=FALSE){
#print("Using table")
ids       <- match(dxy_pop12,as.numeric(names(dxy.table[[1]])))
dxy_pop12 <- dxy.table[[1]][ids]

ids       <- match(dxy_pop13,as.numeric(names(dxy.table[[2]])))
dxy_pop13 <- dxy.table[[2]][ids]

ids       <- match(dxy_pop23,as.numeric(names(dxy.table[[3]])))
dxy_pop23 <- dxy.table[[3]][ids]
}else{
#print("Not using table")
}

d13   <- dxy_pop13 #/(dxy_pop13+dxy_pop23)
d23   <- dxy_pop23 #/(dxy_pop13+dxy_pop23)
d12   <- dxy_pop12
d123  <- dxy_pop123

#d12   <- abs(p-q)
#d13   <- abs(p-freqs[3,])
#d23   <- abs(q-freqs[3,])

alpha   <- (d12-d23)^2
beta    <- (d12-d13)^2
theta   <- (d13-d23)^2

root <- freqs[3,]
root[root!=0] <- 1

# ---------------------------------
BABA    <- ( (p     + alpha )     * ((1-q) + beta) ) * freqs[3,]
ABBA    <- ( ((1-p) + alpha )     * (q     + beta) ) * freqs[3,]

DENOM   <- ABBA + BABA

######################################################

#print(BABA)
sum_ABBA <- sum(ABBA,na.rm=TRUE)
sum_BABA <- sum(BABA,na.rm=TRUE)

#
D     <- (sum_ABBA - sum_BABA)/sum(DENOM, na.rm=TRUE)#(sum_ABBA + sum_BABA) #/valid.sites

#if(keep.site.info){
D_site <- (ABBA - BABA)/DENOM #(ABBA + BABA)
ABBA_site <- ABBA
BABA_site <- BABA

x      <- D_site
Bd_dir <- (sum((d12-d23), na.rm=TRUE) + sum((d12-d13), na.rm=TRUE))

#D <- mean(D_site, na.rm=TRUE)

#}else{
# D_site      <- NULL
# ABBA_site   <- NULL
# BABA_site   <- NULL
#}

# calc f_BD
freqs23    <- freqs[2:3,,drop=FALSE]
maxfreqs23 <- apply(freqs23,2,max)

# f_d denominator
maxBABA <- (d23 * p     + d13 * (1-maxfreqs23))  *maxfreqs23
maxABBA <- (d23 * (1-p) + d13 * maxfreqs23)      *maxfreqs23

sum_maxABBA <- sum(maxABBA, na.rm=TRUE)
sum_maxBABA <- sum(maxBABA, na.rm=TRUE)

f <- (sum_ABBA - sum_BABA)/(sum_maxABBA - sum_maxBABA)

return(list(Bd_dir=Bd_dir, D=D, f=f, D_site=D_site, ABBA=ABBA_site, BABA=BABA_site))

}
```
pievos101/PopGenome documentation built on May 16, 2019, 2:54 a.m.