# Adegenet code # library("ape") library("pegas") library("seqinr") library("ggplot2") library("adegenet") library("hierfstat") ## Import file ## ```myFile <- import2genind("Puma_Stru.stru")``` ### 12444 SNPs and 130 inds library("ape") library("pegas") library("seqinr") library("ggplot2") library("adegenet") library("hierfstat") ?adegenet myFile <- import2genind("Puma_Stru.stru") #12444 SNPs and 130 inds ## QUESTIONS FOR STRUCTURE FILES: ### How many genotypes are there? #### answer: 130 ### How many markers are there? #### answer: 12444 ### Which column contains the populations factor ('0' if absent)? #### answer: 1 ### Which column contains the population factor ('0' if absent)? #### answer:2 ### Which column contains the population factor ('0' if absent)? #### answer: 0 ### Which other optional columns should be read (press 'return' when done)? #### just hit enter ### Which row contains the marker names ('0' if absent)? #### Answer:1 ### Are genotypes coded by a single row (y/n)? #### answer: n ## Look at transformed genind file ``` myFile ``` ## Look at shared alleles ``` myFile2 <- propShared(myFile) genind2df(myFile,sep="|") write.table(myFile2, file = "Output" ) ``` ## finding allelic richness ``` myFile3 <- allel.rich(myFile) write.table(myFile3, file = "allele rich") ``` ## Scaling ``` ?scaleGen X <- scaleGen(myFile, NA.method="zero") X[1:5,1:5] ``` ## Create PCA ``` pca1<-dudi.pca(X,cent=FALSE,scale=FALSE,scannf=FALSE,nf=3) myCol <-c("darkgreen","darkblue") s.class(pca1$li,pop(myFile), col=myCol) ``` ## Making Plot Structure Figures Graph with Q data ### ``` tbl <- read.csv("pumaQdata.csv") barplot(t(as.matrix(tbl)), col=rainbow(3), xlab="Individual #", ylab="Ancestry", border=NA) ``` #### File was then edited in paintscape