PairwiseKinship.jl

PopGen.jl/src/Kinship/PairwiseKinship.jl

📦 not exported	🔵 exported by PopGen.jl

🔵 kinship

kinship(data::PopData; method::Function, iterations::Int = 0, interval::Vector{Float64} = [0.025, 0.975])
kinship(data::PopData, samplenames::AbstractVector{T}; method::Function, iterations::Int = 0, interval::Vector{Float64} = [0.025, 0.975]) where T<:AbstractString

Calculate pairwise relatedness estimates for all or specific individuals in a PopData object using the specified method (see below). Returns a NamedMatrix if not performing bootstrapping, otherwise returns a DataFrame (since bootstrapping provides more output information). To calculate means, median, standard error, and confidence intervals using bootstrapping, set iterations = n where n is an integer greater than 0 (the default) corresponding to the number of bootstrap iterations you wish to perform for each pair. The default confidence interval is [0.0275, 0.975] (i.e. 95%), however that can be changed by supplying a Vector{Float64} of [low, high] to the keyword interval. Note: samples must be diploid.

Arguments

data : A PopData object
samplenames: Vector of sample names (optional)

Keyword Arguments

method::Function : A method function (see below)
iterations::Int64 : The number of iterations to perform bootstrapping (default: 0, will not perform bootstrapping)
interval::Vector{Float64} : A Vector of [low, high] indicating the confidence intervals you would like for bootstrapping (default: [0.275, 0.975], i.e. 95%)

Methods | Method | Type | Method Call | |:----|:-----|:-----| | Blouin 1996 | moments-based | Blouin | | Li & Horvitz 1953| moments-based | LiHorvitz | | Loiselle 1995 | moments-based | Loiselle | | Lynch 1988 | moments-based | Lynch | | Lynch/Li 1993 | moments-based | LynchLi | | Lynch & Ritland 1999 | moments-based | LynchRitland | | Moran 1950 | moments-based | Moran | | Queller & Goodnight 1989 | moments-based | QuellerGoodnight | | Ritland 1996 | moments-based | Ritland |

julia> cats = @nancycats ; 

julia> kin = kinship(cats, method = Moran)
237×237 Named Matrix{Float64}
A ╲ B │         N215          N216  …          N289          N290
──────┼──────────────────────────────────────────────────────────
N215  │ 8.13724e-316       1.62338  …       1.04589       1.15351
N216  │      1.62338       0.29485         0.957724        1.1637
N217  │     0.673577      0.587163         0.547427      0.709887
N218  │     0.896935       0.72942         0.919448      0.791255
⋮                  ⋮             ⋮  ⋱             ⋮             ⋮
N297  │     0.757915      0.858834          1.15432        1.2677
N281  │     0.686057      0.604236         0.942749       1.08762
N289  │      1.04589      0.957724              0.0         1.104
N290  │      1.15351        1.1637  …         1.104           0.0

julia> kinship(cats, method = Moran, iterations = 100)
27966×7 DataFrame
   Row │ sample1  sample2  Moran     bootmean  std       CI_lower      CI_upper 
       │ String   String   Float64   Float64   Float64   Float64       Float64  
───────┼────────────────────────────────────────────────────────────────────────
     1 │ N215     N216     1.62338   0.376626  0.27286    0.00274863   0.916719
     2 │ N215     N217     0.673577  0.202888  0.20094    0.00105976   0.59871
     3 │ N215     N218     0.896935  0.206272  0.232048   7.58373e-5   0.786113
     4 │ N215     N219     0.988931  0.236503  0.221345  -0.00053018   0.718204
   ⋮   │    ⋮        ⋮        ⋮         ⋮         ⋮           ⋮           ⋮
 27964 │ N281     N289     0.942749  0.220475  0.200358   0.001656     0.799307
 27965 │ N281     N290     1.08762   0.285053  0.289967   0.000299019  1.09343
 27966 │ N289     N290     1.104     0.277445  0.235519   0.00186445   0.858206
                                                              27959 rows omitted

📦 _kinship_noboot_nofreq

_kinship_noboot_nofreq(data::PopData, method::Function)

📦 _kinship_noboot_freq

_kinship_noboot_freq(data::PopData, method::Function)

📦 _kinship_boot_nofreq

_kinship_boot_nofreq(data::PopData, method::Function, iterations::Int, interval::Vector{Float64} = [0.025, 0.975])

📦 _kinship_boot_freq

_kinship_boot_freq(data::PopData, method::Function, iterations::Int, interval::Vector{Float64} = [0.025, 0.975])

🔵 kinshiptotable

kinshiptotable(kinshipresults::T, methd::Symbol) where T<:NamedMatrix

Converts the NamedMatrix result from the non-bootstrapped kinship() results into a DataFrame. The second positonal argument (methd) is the name of the value column (default: kinship). For better analysis workflow, it would be useful to specify the method for this column, to keep track of which estimator was used (e.g., Blouin, LynchLi, etc.) Example

julia> cats = @nancycats ; kin = kinship(cats, method = Moran) ;
julia> kinshiptotable(kin, :Moran)
22366×3 DataFrame
   Row │ sample1  sample2  Moran      
       │ String   String   Float64      
───────┼────────────────────────────────
     1 │ cc_001   cc_002    0.00688008
     2 │ cc_001   cc_003   -0.0286812
     3 │ cc_001   cc_005   -0.000749142
     4 │ cc_001   cc_007    0.0516361
     5 │ cc_001   cc_008    0.0261128
     6 │ cc_001   cc_009   -0.00187027
     7 │ cc_001   cc_010    0.0182852
   ⋮   │    ⋮        ⋮          ⋮
 22361 │ seg_028  seg_029  -0.0472928
 22362 │ seg_028  seg_030  -0.0172853
 22363 │ seg_028  seg_031  -0.00240921
 22364 │ seg_029  seg_030  -0.0278483
 22365 │ seg_029  seg_031   0.0297876
 22366 │ seg_030  seg_031  -0.0371295
                      22353 rows omitted

PopGen.jl/src/Kinship/PairwiseKinship.jl​

🔵 kinship​

📦 _kinship_noboot_nofreq​

📦 _kinship_noboot_freq​

📦 _kinship_boot_nofreq​

📦 _kinship_boot_freq​

🔵 kinshiptotable​