ANGSD: Analysis of next generation Sequencing Data
Latest tar.gz version is (0.938/0.939 on github), see Change_log for changes, and download it here.
Fst correctness
3 Populations simulated data
msms -ms 44 10 -t 930 -r 400 -I 3 12 14 18 -n 1 1.682020 -n 2 3.736830 -n 3 7.292050 -eg 0 2 116.010723 -eg 0 3 160.246047 -ma x 0.881098 0.561966 0.881098 x 2.797460 0.561966 2.797460 x -ej 0.028985 3 2 -en 0.028985 2 0.287184 -ema 0.028985 3 x 7.293140 x 7.293140 x x x x x -ej 0.197963 2 1 -en 0.303501 1 1 >msoutput.txt ../misc/msToGlf -in msoutput.txt -out raw -singleOut 1 -regLen 0 -depth 8 -err 0.005 ../misc/splitgl raw.glf.gz 22 1 6 >pop1.glf.gz ../misc/splitgl raw.glf.gz 22 7 13 >pop2.glf.gz ../misc/splitgl raw.glf.gz 22 14 22 >pop3.glf.gz echo \"1 250000000\" >fai.fai ../angsd -glf pop1.glf.gz -nind 6 -doSaf 1 -out pop1 -fai fai.fai -issim 1 ../angsd -glf pop2.glf.gz -nind 7 -doSaf 1 -out pop2 -fai fai.fai -issim 1 ../angsd -glf pop3.glf.gz -nind 9 -doSaf 1 -out pop3 -fai fai.fai -issim 1 ../misc/realSFS pop1.saf.idx >pop1.saf.idx.ml ../misc/realSFS pop2.saf.idx >pop2.saf.idx.ml ../misc/realSFS pop3.saf.idx >pop3.saf.idx.ml ../misc/realSFS pop1.saf.idx pop2.saf.idx -p 20 >pop1.pop2.saf.idx.ml ../misc/realSFS pop1.saf.idx pop3.saf.idx -p 20 >pop1.pop3.saf.idx.ml ../misc/realSFS pop2.saf.idx pop3.saf.idx -p 20 >pop2.pop3.saf.idx.ml ../misc/realSFS fst index pop1.saf.idx pop2.saf.idx -fstout pop1.pop2 -sfs pop1.pop2.saf.idx.ml ../misc/realSFS fst index pop1.saf.idx pop3.saf.idx -fstout pop1.pop3 -sfs pop1.pop3.saf.idx.ml ../misc/realSFS fst index pop2.saf.idx pop3.saf.idx -fstout pop2.pop3 -sfs pop2.pop3.saf.idx.ml ../misc/realSFS fst index pop1.saf.idx pop2.saf.idx pop3.saf.idx -fstout pop1.pop2.pop3 -sfs pop1.pop2.saf.idx.ml -sfs pop1.pop3.saf.idx.ml -sfs pop2.pop3.saf.idx.ml ../misc/realSFS fst stats pop1.pop2.fst.idx ../misc/realSFS fst stats pop1.pop3.fst.idx ../misc/realSFS fst stats pop2.pop3.fst.idx ../misc/realSFS fst stats pop1.pop2.pop3.fst.idx
Which gives the following output
$ ../misc/realSFS fst stats pop1.pop2.fst.idx -> You are printing the optimized SFS to the terminal consider dumping into a file -> E.g.: './realSFS fst stats pop1.pop2.fst.idx >sfs.ml.txt' -> Assuming idxname:pop1.pop2.fst.idx -> Assuming .fst.gz file: pop1.pop2.fst.gz -> FST.Unweight[nObs:51085]:0.114638 Fst.Weight:0.186980 $ ../misc/realSFS fst stats pop1.pop3.fst.idx -> You are printing the optimized SFS to the terminal consider dumping into a file -> E.g.: './realSFS fst stats pop1.pop3.fst.idx >sfs.ml.txt' -> Assuming idxname:pop1.pop3.fst.idx -> Assuming .fst.gz file: pop1.pop3.fst.gz -> FST.Unweight[nObs:51085]:0.121007 Fst.Weight:0.192111 $ ../misc/realSFS fst stats pop2.pop3.fst.idx -> You are printing the optimized SFS to the terminal consider dumping into a file -> E.g.: './realSFS fst stats pop2.pop3.fst.idx >sfs.ml.txt' -> Assuming idxname:pop2.pop3.fst.idx -> Assuming .fst.gz file: pop2.pop3.fst.gz -> FST.Unweight[nObs:51085]:0.069462 Fst.Weight:0.125002 $ ../misc/realSFS fst stats pop1.pop2.pop3.fst.idx -> You are printing the optimized SFS to the terminal consider dumping into a file -> E.g.: './realSFS fst stats pop1.pop2.pop3.fst.idx >sfs.ml.txt' -> Assuming idxname:pop1.pop2.pop3.fst.idx -> Assuming .fst.gz file: pop1.pop2.pop3.fst.gz -> FST.Unweight[nObs:51085]:0.114638 Fst.Weight:0.186980 -> FST.Unweight[nObs:51085]:0.121007 Fst.Weight:0.192111 -> FST.Unweight[nObs:51085]:0.069462 Fst.Weight:0.125002
Two populations (sim data with R implementation of functionality)
nRep <- 100
nPop1 <- 24
nPop2 <- 16
cmd <- paste("msms -ms",nPop1+nPop2,nRep,"-t 930 -r 400 -I 2",nPop1,nPop2,"0 -g 1 9.70406 -n 1 2 -n 2 1 -ma x 0.0 0.0 x -ej 0.07142857 2 1 >msoutput.txt ",sep=" ")
system(cmd)
##system("msms -ms 40 1 -t 930 -r 400 -I 2 20 20 0 -g 1 9.70406 -n 1 2 -n 2 1 -ma x 0.0 0.0 x -ej 0.07142857 2 1 >msoutput.txt ")
source("../R/readms.output.R")
to2dSFS <- function(p1.d,p2.d,nPop1,nPop2)
sapply(0:nPop1,function(x) table(factor(p2.d[p1.d==x],levels=0:nPop2)))
source("../R/readms.output.R")
a<- read.ms.output(file="msoutput.txt")
p1.d <- unlist((sapply(a$gam,function(x) colSums(x[1:nPop1,]))))
p2.d <- unlist((sapply(a$gam,function(x) colSums(x[-c(1:nPop1),]))))
par(mfrow=c(1,2))
barplot(table(p1.d))
barplot(table(p2.d))
sfs.2d <- t(sapply(0:nPop1,function(x) table(factor(p2.d[p1.d==x],levels=0:nPop2))))
system("../misc/msToGlf -in msoutput.txt -out raw -singleOut 1 -regLen 0 -depth 8 -err 0.005")
system("../misc/splitgl raw.glf.gz 20 1 12 >pop1.glf.gz")
system("../misc/splitgl raw.glf.gz 20 13 20 >pop2.glf.gz")
system("echo \"1 250000000\" >fai.fai")
system("../angsd -glf pop1.glf.gz -nind 12 -doSaf 1 -out pop1 -fai fai.fai -issim 1")
system("../angsd -glf pop2.glf.gz -nind 8 -doSaf 1 -out pop2 -fai fai.fai -issim 1")
system("../misc/realSFS pop1.saf.idx >pop1.saf.idx.ml")
system("../misc/realSFS pop2.saf.idx >pop2.saf.idx.ml")
system("../misc/realSFS pop1.saf.idx pop2.saf.idx -maxIter 500 -p 20 >pop1.pop2.saf.idx.ml")
getFst<-function(est){
N1<-nrow(est)-1
N2<-ncol(est)-1
cat("N1: ",N1 ," N2: ",N2,"\n")
est0<-est
est0[1,1]<-0
est0[N1+1,N2+1]<-0
est0<-est0/sum(est0)
aMat<<-matrix(NA,nrow=N1+1,ncol=N2+1)
baMat<<-matrix(NA,nrow=N1+1,ncol=N2+1)
for(a1 in 0:(N1))
for(a2 in 0:(N2)){
p1 <- a1/N1
p2 <- a2/N2
q1 <- 1 - p1
q2 <- 1 - p2
alpha1 <- 1 - (p1^2 + q1^2)
alpha2 <- 1 - (p2^2 + q2^2)
al <- 1/2 * ( (p1-p2)^2 + (q1-q2)^2) - (N1+N2) * (N1*alpha1 + N2*alpha2) / (4*N1*N2*(N1+N2-1))
bal <- 1/2 * ( (p1-p2)^2 + (q1-q2)^2) + (4*N1*N2-N1-N2)*(N1*alpha1 + N2*alpha2) / (4*N1*N2*(N1+N2-1))
aMat[a1+1,a2+1]<<-al
baMat[a1+1,a2+1]<<-bal
## print(signif(c(a1=a1,a2=a2,p1=p1,p2=p2,al1=alpha1,al2=alpha2,al),2))
}
## unweighted average of single-locus ratio estimators
fstU <- sum(est0*(aMat/baMat),na.rm=T)
## weighted average of single-locus ratio estimators
fstW <- sum(est0*aMat,na.rm=T)/sum(est0*baMat,na.rm=T)
c(fstW=fstW,fstU=fstU)
}
> getFst(sfs.2d)
N1: 24 N2: 16
fstW fstU
0.11945801 0.08249571
est <- matrix(as.integer(scan("pop1.pop2.saf.idx.ml")),byrow=T,ncol=nPop2+1)
> getFst(est)
N1: 24 N2: 16
fstW fstU
0.11925903 0.08241461
cmd<"fst index pop1.saf.idx pop2.saf.idx -sfs pop1.pop2.saf.idx.ml -fstout testing"
system(cmd)
##view the per site 'alpha' 'beta' if you want
cmd<-"../misc/realSFS fst print testing.fst.idx |head"
##use fancy new emperical bayes
cmd<- "../misc/realSFS fst stats testing.fst.idx "
system(cmd)
-> FST.Unweight:0.083316 Fst.Weight:0.119372