sgsR is a package for calculating spatial genetic structure in R. The aim is to implement analyses, similar to those found in SPAGeDi and GenAlEx, that estimate the degree of spatial autocorrelation in genetic data.
Some key features of sgsR are:
Here's an example of a typical workflow...
library(sgsR) ## Simulate genetic data Nind = 100 Nloci = 10 Nallele = 10 n = Nind * 2 # Number of gene copies ## Initialize data frame dat <- data.frame(id = 0:(Nind-1)) dat$x = runif(Nind, 0, 100) dat$y = runif(Nind, 0, 100) ## Simulate Random genetic data for(loci in 1:Nloci){ loci_name_a = paste("Loc", loci, "_A", sep = "") loci_name_b = paste("Loc", loci, "_B", sep = "") dat[loci_name_a] <- sample.int(Nallele, Nind, replace = TRUE) dat[loci_name_b] <- sample.int(Nallele, Nind, replace = TRUE) } ## Convert to sgsObj sgsObj = createSgsObj(sample_ids = dat$id, genotype_data = dat[, 4:(Nloci*2 + 3)], ploidy = 2, x_coords = dat$x, y_coords = dat$y) # Display genetic data head(sgsObj$gen_data) ## Run analysis distance_intervals = seq(10, 110, 10) # Set distance intervals out1 = sgs(sgsObj = sgsObj, distance_intervals = distance_intervals, nperm = 99) ## Plotting results ## Solid line is Fij estimate for each distance class ## Dashed lines are the 2.5 % and 97.5 % quantiles of the permuted values plot(out1) # Summary of information on distance classes out1$di # Summary of information on estimated Kinship coefficient for each distance class (columns) round(out1$fij_obs, 3)
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