Dear all,



I would like to stay in touch with those involved with landscape genetics.
My interest is mainly to assess how simulated animal movements can influence
genetic diversity. We are simulating animal movement using different
landscape patterns, and each male-female meeting get a chance of genes
exchange. So, on the beginning of the simulation we define a loci structure
like: LOCI_struct_start=[ [0,1,0,0,1,1] , [0,1,0,1] ] where we can see to
locus with different number of alleles (6 and 4). When female meet a male,
there are a chance of female pass the genes combination to offspring, like
LOCI_struct_end=[ [1,1,0,1,1,1] , [0,0,0,1] ]. My interest is to estimate
both the genes diversity for each “female/potential offspring”, and for the
whole “population” (all individual simulated on a specific landscape
condition).



Below you can find a sample of my data-structure. I would be happy if
someone give me some adviser on which package/function to use, or some book
that explain Fst, H0, He analysis using R/Bioconductor packages.



library(rjson)

myDF<-data.frame(cbind(x=c("[[1, 0, 0], [0, 1]]",

   "[[1, 1, 0], [0, 1]]","[[1, 0, 0], [1, 1]]",

   "[[0, 0, 1], [0, 1]]")))

myDF

lets = unlist(lapply(fromJSON(as.character(myDF[1,])),length))

lets

rep(LETTERS[1:length(lets)],lets)

sequence(lets)

col_names_letters<-paste(rep(LETTERS[1:length(lets)],lets),sequence(lets),sep="")



myDF_new<-NULL

for (i in 1:nrow(myDF))

 {

 x<-c(as.character(myDF[i,"x"]))

 fromJSON(x)

 unlist(fromJSON(x))

 aux_DF<-data.frame(t(cbind(unlist(fromJSON(x)))))

 myDF_new<-data.frame(rbind(myDF_new,aux_DF))

 }

colnames(myDF_new)<-col_names_letters

myDF_new



cheers



Milton

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