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:
- Calculating relatedness among individuals based on set distance intervals
- Conducting permutation tests
- Creating spatial autocorrelation plots
- Reading and converting from SPAGeDi data format
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If you're interested in contributing, have any comments or suggestions, please get in touch via github or email - lukembrowne@gmail.com
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To install sgsR, you must first make sure the package 'devtools' is installed. This will allow you to install sgsR directly from github.
install.packages("devtools")
devtools::install_github("lukembrowne/sgsR")=================
Here's an example of a typical workflow...
- Input data into the sgs data structure using createSgsObj()
- Set desired distance intervals and number of permutations
- Run SGS analysis with sgs(), currently configured to run the kinship coefficient of Loiselle et al. 1995
- use plotSgs() to produce an autorcorrelation plot
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)
#> Loc1_A Loc1_B Loc2_A Loc2_B Loc3_A Loc3_B Loc4_A Loc4_B Loc5_A Loc5_B
#> 1 5 2 9 4 4 4 7 1 1 2
#> 2 3 9 10 10 5 9 9 4 10 6
#> 3 5 2 8 4 10 9 2 9 5 3
#> 4 7 5 10 6 10 2 10 1 6 6
#> 5 8 5 3 6 4 7 1 3 9 5
#> 6 4 4 4 10 8 5 5 6 5 5
#> Loc6_A Loc6_B Loc7_A Loc7_B Loc8_A Loc8_B Loc9_A Loc9_B Loc10_A Loc10_B
#> 1 5 9 5 5 3 3 2 2 2 6
#> 2 8 10 1 9 4 8 4 9 5 6
#> 3 2 10 10 7 5 8 7 10 7 9
#> 4 2 8 8 1 4 2 4 7 4 4
#> 5 5 9 1 3 7 8 10 2 3 9
#> 6 8 6 9 3 8 4 10 9 1 8
## Run analysis
distance_intervals = seq(10, 110, 10) # Set distance intervals
out1 = sgs(sgsObj = sgsObj, distance_intervals = distance_intervals, nperm = 99)
#> Adding an aditional distance interval -- 130.1137 -- to encompass all pairwise distances..
#> Working on permutation: 0...
## 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
plotSgs(out1)
# Summary of information on distance classes
round(out1$DIsummary, 3)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> Distance class 0.000 1.000 2.000 3.000 4.000 5.000 6.000
#> Max distance 10.000 20.000 30.000 40.000 50.000 60.000 70.000
#> Average distance 6.407 15.578 25.245 35.046 45.027 55.091 65.033
#> Number of pairs 140.000 365.000 540.000 615.000 686.000 683.000 609.000
#> [,8] [,9] [,10] [,11] [,12]
#> Distance class 7.000 8.000 9.000 10.000 11.000
#> Max distance 80.000 90.000 100.000 110.000 130.114
#> Average distance 74.986 84.783 94.563 104.218 116.444
#> Number of pairs 520.000 381.000 244.000 114.000 53.000
# Summary of information on estimated Kinship coefficient for each distance class (columns)
round(out1$Fijsummary, 3)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> Loc1_A 0.002 0.017 -0.003 0.009 -0.005 -0.001 -0.004 0.005 0.001
#> Loc2_A 0.014 -0.015 0.001 -0.001 0.002 -0.001 -0.004 0.002 0.008
#> Loc3_A -0.002 0.005 0.001 0.001 -0.004 -0.002 0.000 0.003 -0.001
#> Loc4_A -0.018 -0.001 0.009 -0.006 0.007 -0.011 -0.006 0.002 0.012
#> Loc5_A -0.028 -0.008 0.002 0.011 -0.005 -0.001 0.002 0.012 -0.003
#> Loc6_A 0.010 0.011 0.000 -0.004 -0.001 -0.001 -0.010 0.010 -0.002
#> Loc7_A -0.002 -0.003 -0.011 -0.002 0.010 0.000 0.000 0.006 -0.001
#> Loc8_A 0.001 0.004 0.011 -0.003 -0.002 0.003 -0.002 0.003 -0.006
#> Loc9_A 0.034 0.004 0.004 -0.005 -0.001 -0.001 0.007 -0.008 -0.015
#> Loc10_A -0.014 -0.005 0.001 -0.007 0.005 0.002 -0.002 0.005 -0.005
#> ALL LOCI 0.000 0.001 0.002 -0.001 0.001 -0.001 -0.002 0.004 -0.001
#> [,10] [,11] [,12]
#> Loc1_A -0.011 -0.019 -0.026
#> Loc2_A 0.010 0.001 -0.013
#> Loc3_A -0.012 0.021 -0.015
#> Loc4_A 0.006 0.010 -0.025
#> Loc5_A -0.009 -0.017 -0.010
#> Loc6_A 0.005 -0.029 0.030
#> Loc7_A 0.001 -0.012 0.013
#> Loc8_A -0.005 -0.015 -0.035
#> Loc9_A 0.004 0.004 -0.003
#> Loc10_A 0.002 0.004 0.034
#> ALL LOCI -0.001 -0.005 -0.005=================