ANGSD: Analysis of next generation Sequencing Data

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;THIS PAGE IS OBSOLETE, PLEASE SEE FST AND PCA in the sidebar for the latest versions
Matteo Fumagalli has been working on methods to estimate Fst and doing PCA/Covariance based on ANGSD output files.
Matteo Fumagalli has been working on methods to estimate Fst and doing PCA/Covariance based on ANGSD output files.


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https://github.com/mfumagalli/ngsTools
https://github.com/mfumagalli/ngsTools


We are also working on a new implementation that generalizes the above approach to multiple populations.
=Fst=
 
=New Fancy Version=
 
==Two Populations real data==
<pre>
#this is with 2pops
#first calculate per pop saf for each populatoin
../angsd -b list1  -anc hg19ancNoChr.fa -out pop1 -dosaf 1 -gl 1
../angsd -b list2  -anc hg19ancNoChr.fa -out pop1 -dosaf 1 -gl 1
#calculate the 2dsfs prior
../misc/realSFS pop1.saf.idx pop2.saf.idx >pop1.pop2.ml
#prepare the fst for easy window analysis etc
../misc/realSFS fst index pop1.saf.idx pop2.saf.idx -sfs pop1.pop2.ml -fstout here
#get the global estimate
../misc/realSFS fst stats here.fst.idx
-> FST.Unweight:0.069395 Fst.Weight:0.042349
#below is not tested that much, but seems to work
../misc/realSFS fst stats here.fst.idx -win 50000 -step 10000 >slidingwindow
</pre>
==3 Populations real data==
<pre>
#this is with 2pops
#first calculate per pop saf for each populatoin
../angsd -b list10  -anc hg19ancNoChr.fa -out pop1 -dosaf 1 -gl 1
../angsd -b list11  -anc hg19ancNoChr.fa -out pop2 -dosaf 1 -gl 1
../angsd -b list12  -anc hg19ancNoChr.fa -out pop3 -dosaf 1 -gl 1
#calculate the 2dsfs prior
../misc/realSFS pop1.saf.idx pop2.saf.idx >pop1.pop2.ml
#prepare the fst for easy window analysis etc
../misc/realSFS fst index pop1.saf.idx pop2.saf.idx -sfs pop1.pop2.ml -fstout here
#get the global estimate
../misc/realSFS fst stats here.fst.idx
-> FST.Unweight:0.069395 Fst.Weight:0.042349
#below is not tested that much, but seems to work
../misc/realSFS fst stats here.fst.idx -win 50000 -step 10000 >slidingwindow
</pre>
 
== 3 Populations simulated data==
<pre>
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
</pre>
Which gives the following output
<pre>
$ ../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
</pre>
 
==Two populations (sim data with R implementation of functionality)==
<pre>
  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
</pre>
 
=Old nice version=
==Fst==
# Generate .saf files from each population using ANGSD [[SFS Estimation]]
# Generate .saf files from each population using ANGSD [[SFS Estimation]]
## using a 2D-SFS as a prior, estimated using ngs2dSFS
## using a 2D-SFS as a prior, estimated using ngs2dSFS
## using marginal spectra as priors, estimated using '''realSFS'''
## using marginal spectra as priors, estimated using '''realSFS'''


==PCA==
=PCA=
More information here:
More information here:
https://github.com/mfumagalli/ngsTools#ngscovar
https://github.com/mfumagalli/ngsTools#ngscovar

Latest revision as of 15:21, 30 July 2015

THIS PAGE IS OBSOLETE, PLEASE SEE FST AND PCA in the sidebar for the latest versions

Matteo Fumagalli has been working on methods to estimate Fst and doing PCA/Covariance based on ANGSD output files.

The main documentation for this is found here: https://github.com/mfumagalli/ngsTools

Fst

  1. Generate .saf files from each population using ANGSD SFS Estimation
    1. using a 2D-SFS as a prior, estimated using ngs2dSFS
    2. using marginal spectra as priors, estimated using realSFS

PCA

More information here: https://github.com/mfumagalli/ngsTools#ngscovar

cite

If you use these methods, you should cite the m. fumagalli paper http://www.ncbi.nlm.nih.gov/pubmed/23979584

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