Genetic

src/Diversity.jl

@@ -18,7 +18,7 @@ not automatically exported (as we feel they are too short) and with
 matching longer ASCII names (e.g. ```NormalisedAlpha()```), which are.
 We also provide functions to calculate appropriate
 ```subcommunityDiversity()``` and ```metacommunityDiversity()```
-values for each measure, a general ```diversity()``` function for
+values for each measure, and a general ```diversity()``` function to
 extract any diversity measure at a series of scales.
 """
 module Diversity
@@ -93,6 +93,9 @@ export RawAlpha, NormalisedAlpha
 export RawBeta, NormalisedBeta, RawRho, NormalisedRho
 export Gamma
 
+include("Genetics.jl")
+export geneDistance
+
 """
  Diversity.ShortNames submodule
 

src/Genetics.jl

@@ -0,0 +1,68 @@
+using Diversity
+using Diversity.API
+
+using PopGen
+using StringDistances
+using LinearAlgebra
+
+using FASTX
+
+abstract type AbstractGeneticTypes{PopData} <: 
+ Diversity.API.AbstractTypes
+end
+
+struct GeneticType{PopData} <: AbstractGeneticTypes{PopData}
+ dat::PopData
+ ntypes::Int64
+ Zmatrix::Matrix{Float64}
+end
+
+function _hammingDistance(geno1, geno2)
+ ismissing(geno1) || ismissing(geno2) && return missing
+ if length(geno1) > 2
+ @warn "hamming_distance may not work correctly for ploidy > 2"
+ end
+ #TODO Fix ploidy > 2 - e.g. (1, 1, 1, 2) ≠ (1, 2, 2, 2)
+
+ max(sum(geno1 .∉ Ref(geno2)), sum(geno2 .∉ Ref(geno1)))
+end
+
+function GeneticType(dat::PopData) 
+ # Initialise objects
+ matrix_obj = PopGen.loci_matrix(dat)
+ ntypes = size(matrix_obj, 1)
+ output = zeros(Float64, ntypes, ntypes)
+ indices = PopGen.pairwise_pairs(1:ntypes)
+
+ # Calculate distance matrix
+ for (a, b) in indices
+ output[a, b] = sum(_hammingDistance.((@view matrix_obj[a, :]),
+ (@view matrix_obj[b, :])))
+ end
+ dist = Symmetric(output)
+ dist /= maximum(dist)
+
+ # Calculate similarity matrix
+ Zmatrix = 1 .- dist
+
+ return GeneticType{PopData}(dat, ntypes, Zmatrix)
+end
+
+function GeneticType(dat::Vector) # Vector{BioSequences.AminoAcidSequence}
+ # Initialise objects
+ ntypes = length(dat)
+ output = zeros(Int64, ntypes, ntypes)
+ indices = PopGen.pairwise_pairs(1:ntypes)
+
+ # Calculate distance matrix
+ for (a, b) in indices
+ output[a, b] = evaluate(Hamming(), dat[a], dat[b])
+ end
+ dist = Symmetric(output)
+ dist /= maximum(dist)
+
+ # Calculate similarity matrix
+ Zmatrix = 1 .- dist
+
+ return GeneticType{BioSequences.AminoAcidSequence}(dat, ntypes, Zmatrix)
+end