From 93564b247e7a6b5ee013d5afe0a40d6d1c09a768 Mon Sep 17 00:00:00 2001 From: "Thomas A. Christensen II" <25492070+MillironX@users.noreply.github.com> Date: Sat, 19 Jun 2021 18:27:52 -0500 Subject: [PATCH] Reformat document --- src/BeefBLUP.jl | 341 ++++++++++++++++++++++++------------------------ 1 file changed, 170 insertions(+), 171 deletions(-) diff --git a/src/BeefBLUP.jl b/src/BeefBLUP.jl index a2dbfe1..bcfb815 100755 --- a/src/BeefBLUP.jl +++ b/src/BeefBLUP.jl @@ -18,32 +18,32 @@ using Gtk # Main entry-level function - acts just like the script function beefblup() -# Ask for an input spreadsheet -path = open_dialog_native( + # Ask for an input spreadsheet + path = open_dialog_native( "Select a beefblup worksheet", GtkNullContainer(), ("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet")) -) + ) -# Ask for an output text filename -savepath = save_dialog_native( + # Ask for an output text filename + savepath = save_dialog_native( "Save your beefblup results", GtkNullContainer(), (GtkFileFilter("*.txt", name="Results file"), "*.txt") -) + ) -# Ask for heritability -print("What is the heritability for this trait?> ") -h2 = parse(Float64, readline(stdin)) + # Ask for heritability + print("What is the heritability for this trait?> ") + h2 = parse(Float64, readline(stdin)) -beefblup(path, savepath, h2) + beefblup(path, savepath, h2) end function beefblup(datafile::String, h2::Float64) # Assume the data is named the same as the file without the trailing extension - dataname = join(split(datafile, ".")[1:end-1]) + dataname = join(split(datafile, ".")[1:end - 1]) # Create a new results name resultsfile = string(dataname, "_results.txt") @@ -55,211 +55,210 @@ end # Main worker function, can perform all the work if given all the user input function beefblup(path::String, savepath::String, h2::Float64) -# Import data from a suitable spreadsheet -data = DataFrame(CSV.File(path)) + # Import data from a suitable spreadsheet + data = DataFrame(CSV.File(path)) -# Sort the array by date -sort!(data, :birthdate) + # Sort the array by date + sort!(data, :birthdate) -# Define fields to hold id values for animals and their parents -numanimals = length(data.id) + # Define fields to hold id values for animals and their parents + numanimals = length(data.id) -# Find the index values for animals and their parents -dam = indexin(data.dam, data.id) -sire = indexin(data.sire, data.id) + # Find the index values for animals and their parents + dam = indexin(data.dam, data.id) + sire = indexin(data.sire, data.id) -# Extract all of the fixed effects -fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end-1] + # Extract all of the fixed effects + fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end - 1] -# Find any columns that need to be deleted -for i in 1:ncol(fixedfx) - if length(unique(fixedfx[:,i])) <= 1 - @warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis") - DataFrames.select!(fixedfx,Not(i)) - end -end - -# Determine how many contemporary groups there are -numtraits = ncol(fixedfx) -numgroups = ones(1, numtraits) -for i in 1:numtraits - numgroups[i] = length(unique(fixedfx[:,i])) -end - -# If there are more groups than animals, then the analysis cannot continue -if sum(numgroups) >= numanimals - throw(ErrorException("there are more contemporary groups than animals")) -end - -# Define a "normal" animal as one of the last in the groups, provided that -# all traits do not have null values -normal = Array{String}(undef,1,numtraits) -for i in 1:numtraits - for j in numanimals:-1:1 - if !ismissing(fixedfx[j,i]) - normal[i] = string(fixedfx[j,i]) - break + # Find any columns that need to be deleted + for i in 1:ncol(fixedfx) + if length(unique(fixedfx[:,i])) <= 1 + @warn string("column '", names(fixedfx)[i], "' does not have any unique animals and will be removed from this analysis") + DataFrames.select!(fixedfx, Not(i)) end end -end -# Form the fixed-effect matrix -X = zeros(Int8, numanimals, floor(Int,sum(numgroups))-length(numgroups)+1) -X[:,1] = ones(Int8, 1, numanimals) + # Determine how many contemporary groups there are + numtraits = ncol(fixedfx) + numgroups = ones(1, numtraits) + for i in 1:numtraits + numgroups[i] = length(unique(fixedfx[:,i])) + end -# Create an external counter that will increment through both loops -counter = 2 + # If there are more groups than animals, then the analysis cannot continue + if sum(numgroups) >= numanimals + throw(ErrorException("there are more contemporary groups than animals")) + end -# Store the traits in a string array -adjustedtraits = -Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups)) -# Iterate through each group -for i in 1:length(normal) - # Find the traits that are present in this trait - localdata = string.(fixedfx[:,i]) - traits = unique(localdata) - # Remove the normal version from the analysis - effecttraits = traits[findall(x -> x != normal[i], traits)] - # Iterate inside of the group - for j in 1:(length(effecttraits)) + # Define a "normal" animal as one of the last in the groups, provided that + # all traits do not have null values + normal = Array{String}(undef, 1, numtraits) + for i in 1:numtraits + for j in numanimals:-1:1 + if !ismissing(fixedfx[j,i]) + normal[i] = string(fixedfx[j,i]) + break + end + end + end + + # Form the fixed-effect matrix + X = zeros(Int8, numanimals, floor(Int, sum(numgroups)) - length(numgroups) + 1) + X[:,1] = ones(Int8, 1, numanimals) + + # Create an external counter that will increment through both loops + counter = 2 + + # Store the traits in a string array + adjustedtraits = + Array{String}(undef,floor(Int, sum(numgroups)) - length(numgroups)) + # Iterate through each group + for i in 1:length(normal) + # Find the traits that are present in this trait + localdata = string.(fixedfx[:,i]) + traits = unique(localdata) + # Remove the normal version from the analysis + effecttraits = traits[findall(x -> x != normal[i], traits)] + # Iterate inside of the group + for j in 1:(length(effecttraits)) matchedindex = findall(x -> x == effecttraits[j], localdata) X[matchedindex, counter] .= 1 # Add this trait to the string adjustedtraits[counter - 1] = traits[j] - # Increment the big counter - counter = counter + 1 - end -end - -# Create an empty matrix for the additive relationship matrix -A = zeros(numanimals, numanimals) - -# Create the additive relationship matrix by the FORTRAN method presented by -# Henderson -for i in 1:numanimals - if !isnothing(dam[i]) && !isnothing(sire[i]) - for j in 1:(i-1) - A[j,i] = 0.5*(A[j,sire[i]] + A[j,dam[i]]) - A[i,j] = A[j,i] + # Increment the big counter + counter = counter + 1 end - A[i,i] = 1 + 0.5*A[sire[i], dam[i]] - elseif !isnothing(dam[i]) && isnothing(sire[i]) - for j in 1:(i-1) - A[j,i] = 0.5*A[j,dam[i]] + end + + # Create an empty matrix for the additive relationship matrix + A = zeros(numanimals, numanimals) + + # Create the additive relationship matrix by the FORTRAN method presented by + # Henderson + for i in 1:numanimals + if !isnothing(dam[i]) && !isnothing(sire[i]) + for j in 1:(i - 1) + A[j,i] = 0.5 * (A[j,sire[i]] + A[j,dam[i]]) + A[i,j] = A[j,i] + end + A[i,i] = 1 + 0.5 * A[sire[i], dam[i]] + elseif !isnothing(dam[i]) && isnothing(sire[i]) + for j in 1:(i - 1) + A[j,i] = 0.5 * A[j,dam[i]] A[i,j] = A[j,i] end A[i,i] = 1 elseif isnothing(dam[i]) && !isnothing(sire[i]) - for j in 1:(i-1) - A[j,i] = 0.5*A[j,sire[i]] + for j in 1:(i - 1) + A[j,i] = 0.5 * A[j,sire[i]] A[i,j] = A[j,i] end A[i,i] = 1 else - for j in 1:(i-1) + for j in 1:(i - 1) A[j,i] = 0 A[i,j] = 0 end A[i,i] = 1 + end end -end -# Extract the observed data -Y = convert(Array{Float64}, data[:,end]) + # Extract the observed data + Y = convert(Array{Float64}, data[:,end]) -# The random effects matrix -Z = Matrix{Int}(I, numanimals, numanimals) + # The random effects matrix + Z = Matrix{Int}(I, numanimals, numanimals) -# Remove items where there is no data -nullobs = findall(isnothing, Y) -Z[nullobs, nullobs] .= 0 + # Remove items where there is no data + nullobs = findall(isnothing, Y) + Z[nullobs, nullobs] .= 0 -# Calculate heritability -λ = (1-h2)/h2 + # Calculate heritability + λ = (1 - h2) / h2 -# Use the mixed-model equations -MME = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*λ)] -MMY = [X'*Y; Z'*Y] -solutions = MME\MMY + # Use the mixed-model equations + MME = [X' * X X' * Z; Z' * X (Z' * Z) + (inv(A) .* λ)] + MMY = [X' * Y; Z' * Y] + solutions = MME \ MMY -# Find the accuracies -diaginv = diag(inv(MME)) -reliability = ones(Float64, length(diaginv)) - diaginv.*λ + # Find the accuracies + diaginv = diag(inv(MME)) + reliability = ones(Float64, length(diaginv)) - diaginv .* λ -# Find how many traits we found BLUE for -numgroups = numgroups .- 1 + # Find how many traits we found BLUE for + numgroups = numgroups .- 1 -# Extract the names of the traits -fixedfxnames = names(fixedfx) -traitname = names(data)[end] + # Extract the names of the traits + fixedfxnames = names(fixedfx) + traitname = names(data)[end] -# Start printing results to output -fileID = open(savepath, "w") -write(fileID, "beefblup Results Report\n") -write(fileID, "Produced using beefblup (") -write(fileID, "https://github.com/millironx/beefblup") -write(fileID, ")\n\n") -write(fileID, "Input:\t") -write(fileID, path) -write(fileID, "\nAnalysis performed:\t") -write(fileID, string(Dates.today())) -write(fileID, "\nTrait examined:\t") -write(fileID, traitname) -write(fileID, "\n\n") + # Start printing results to output + fileID = open(savepath, "w") + write(fileID, "beefblup Results Report\n") + write(fileID, "Produced using beefblup (") + write(fileID, "https://github.com/millironx/beefblup") + write(fileID, ")\n\n") + write(fileID, "Input:\t") + write(fileID, path) + write(fileID, "\nAnalysis performed:\t") + write(fileID, string(Dates.today())) + write(fileID, "\nTrait examined:\t") + write(fileID, traitname) + write(fileID, "\n\n") -# Print base population stats -write(fileID, "Base Population:\n") -for i in 1:length(normal) - write(fileID, "\t") - write(fileID, fixedfxnames[i]) - write(fileID, ":\t") - write(fileID, normal[i]) - write(fileID, "\n") -end -write(fileID, "\tMean ") -write(fileID, traitname) -write(fileID, ":\t") -write(fileID, string(solutions[1])) -write(fileID, "\n\n") - -# Contemporary group adjustments -counter = 2 -write(fileID, "Contemporary Group Effects:\n") -for i in 1:length(numgroups) - write(fileID, "\t") - write(fileID, fixedfxnames[i]) - write(fileID, "\tEffect\tReliability\n") - for j in 1:numgroups[i] + # Print base population stats + write(fileID, "Base Population:\n") + for i in 1:length(normal) write(fileID, "\t") - write(fileID, adjustedtraits[counter - 1]) - write(fileID, "\t") - write(fileID, string(solutions[counter])) - write(fileID, "\t") - write(fileID, string(reliability[counter])) + write(fileID, fixedfxnames[i]) + write(fileID, ":\t") + write(fileID, normal[i]) write(fileID, "\n") + end + write(fileID, "\tMean ") + write(fileID, traitname) + write(fileID, ":\t") + write(fileID, string(solutions[1])) + write(fileID, "\n\n") - counter = counter + 1 + # Contemporary group adjustments + counter = 2 + write(fileID, "Contemporary Group Effects:\n") + for i in 1:length(numgroups) + write(fileID, "\t") + write(fileID, fixedfxnames[i]) + write(fileID, "\tEffect\tReliability\n") + for j in 1:numgroups[i] + write(fileID, "\t") + write(fileID, adjustedtraits[counter - 1]) + write(fileID, "\t") + write(fileID, string(solutions[counter])) + write(fileID, "\t") + write(fileID, string(reliability[counter])) + write(fileID, "\n") + + counter = counter + 1 + end + write(fileID, "\n") end write(fileID, "\n") -end -write(fileID, "\n") -# Expected breeding values -write(fileID, "Expected Breeding Values:\n") -write(fileID, "\tID\tEBV\tReliability\n") -for i in 1:numanimals - write(fileID, "\t") - write(fileID, string(data.id[i])) - write(fileID, "\t") - write(fileID, string(solutions[i+counter-1])) - write(fileID, "\t") - write(fileID, string(reliability[i+counter-1])) - write(fileID, "\n") -end - -write(fileID, "\n - END REPORT -") -close(fileID) + # Expected breeding values + write(fileID, "Expected Breeding Values:\n") + write(fileID, "\tID\tEBV\tReliability\n") + for i in 1:numanimals + write(fileID, "\t") + write(fileID, string(data.id[i])) + write(fileID, "\t") + write(fileID, string(solutions[i + counter - 1])) + write(fileID, "\t") + write(fileID, string(reliability[i + counter - 1])) + write(fileID, "\n") + end + write(fileID, "\n - END REPORT -") + close(fileID) end end