sgsR

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:

1. Calculating relatedness among individuals based on set distance intervals
2. Conducting permutation tests
3. Creating spatial autocorrelation plots
4. 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 - [email protected]

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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")

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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

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