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