beefblup/MATLAB/beefblup.m

% beefblup
% Main script for performing single-variate BLUP to find beef cattle
% breeding values
% Usage: beefblup
% (C) 2018 Thomas A. Christensen II
% Licensed under BSD-3-Clause License

% Prepare the workspace for computation
clear
clc
close all

%% Display stuff
disp('beefblup v. 0.0.0.1')
disp('(C) 2018 Thomas A. Christensen II')
disp('https://github.com/millironx/beefblup')
disp(' ')

%% Prompt User
% Ask for an input spreadsheet
[name, path] = uigetfile('*.xlsx','Select a beefblup worksheet');

% Ask for an ouput text file
[savename, savepath, ~] = uiputfile('*.txt', 'Save your beefblup results', 'results');

% Ask for heritability
h2 = input('What is the heritability for this trait? >> ');


%% Import input file
tic
disp(' ')
disp('Importing Excel file...')

% Import data from a suitable spreadsheet
fullname = [path name];
clear name path
[~, ~, data] = xlsread(fullname);

disp('Importing Excel file... Done!')
toc
disp(' ')

%% Process input file
tic
disp(' ')
disp('Processing and formatting data...')
disp(' ')

% Extract the headers into a separate array
headers = data(1,:);
data(1,:) = [];

% Convert the string dates to numbers
data(:,2) = num2cell(datenum(data(:,2)));

% Sort the array by date
data = sortrows(data,2);

% Coerce all id fields to string format
strings = [1 3 4];
data(:,strings) = cellfun(@num2str, data(:,strings), 'UniformOutput', false);

% Create a lookup lambda function to find the animal represented by a
% certain id
animal2row = @(id) find(strcmp(data(:,1), id));
row2animal = @(rownum) [data{rownum,1}];
ids = [data{:,1}];
numanimals = length(data(:,1));

% Store column numbers that need to be deleted
% Column 6 contains an intermediate Excel calculation and always needs to
% be deleted
colstodelete = 6;

% Coerce each group to string format
for i = 7:length(headers)
   data(:,i) = cellfun(@num2str, data(:,i), 'UniformOutput', false); 
end

% Find any columns that need to be deleted
for i = 7:length(headers)
    if length(uniquecell(data(:,i))) <= 1
        colname = headers{i};
        disp(['Column "' colname '" does not have any unique animals and will be removed'])
        disp('from this analysis');
        colstodelete = [colstodelete i]; 
    end
end

% Delete the appropriate columns from the datasheet and the headers
data(:,colstodelete) = [];
headers(colstodelete) = [];

% Determine how many contemporary groups there are
numgroups = ones(1, length(headers)-5);
for i = 6:length(headers)
    numgroups(i-5) = length(uniquecell(data(:,i)));
end

% If there are more groups than animals, then the analysis cannot continue
if sum(numgroups) >= numanimals
   disp('There are more contemporary groups than animals. The analysis will now abort.');
   return
end

% Define a "normal" animal as one of the last in the groups, provided that
% all traits do not have null values
normal = cell([1 length(headers)-5]);
for i = 6:length(headers)
   for j = numanimals:-1:1
      if not(cellfun(@isempty, data(j,i)))
         normal(i - 5) = data(j,i);
         break
      end
   end
end

% Print the results of the "normal" definition
disp(' ')
disp('For the purposes of this analysis, a "normal" animal will be defined')
disp('by the following traits:')
for i = 6:length(headers)
    disp([headers{i} ': ' normal{i-5}])
end
disp(' ')
disp('If no animal matching this description exists, the results may appear')
disp('outlandish, but are still as correct as the accuracy suggests')
disp(' ')

disp('Processing and formatting data... Done!')
toc
disp(' ')

%% Create the fixed-effect matrix
tic
disp(' ')
disp('Creating the fixed-effect matrix...')

% Form the fixed effect matrix
X = zeros(numanimals, sum(numgroups)-length(numgroups)+1);
X(:,1) = ones(1, numanimals);

% Create an external counter that will increment through both loops
I = 2;

% Store the traits in a string cell array
adjustedtraits = cell(1, sum(numgroups)-length(numgroups));

% Iterate through each group
for i = 1:length(normal)
    % Find the traits that are present in this trait
    traits = uniquecell(data(:,i+5));
    
    % Remove the "normal" version from the analysis
    normalindex = find(strcmp(traits, normal{i}));
    traits(normalindex)  = [];
    
    % Iterate inside of the group
    for j = 1:length(traits)
        matchedindex = find(strcmp(data(:,i+5), traits{j}));
        X(matchedindex, I) = 1;
        
        % Add this trait to the string
        adjustedtraits(I - 1) = traits(j);
        
        % Increment the big counter
        I = I + 1;
    end
end

disp('Creating the fixed-effect matrix... Done!')
toc
disp(' ')

%% Additive relationship matrix
tic
disp(' ')
disp('Creating the additive relationship matrix...')

% Create an empty matrix for the additive relationship matrix
A = zeros(numanimals, numanimals);

% Create lambdas to find sire and dam of each animal
id2dam = @(id) [data{animal2row(num2str(id)), 3}];
id2sire = @(id) [data{animal2row(num2str(id)), 4}];
row2dam = @(rownum) [data{rownum, 3}];
row2sire = @(rownum) [data{rownum, 4}];
rowofdam = @(id) animal2row(id2dam(id));
rowofsire = @(id) animal2row(id2sire(id));
row2rowofdam = @(rownum) rowofdam(row2animal(rownum));
row2rowofsire = @(rownum) rowofsire(row2animal(rownum));

% Create the additive relationship matrix by the FORTRAN method presented
% by Henderson
for i = 1:numanimals
   id = row2animal(i);
   if isempty(row2rowofsire(i)) && isempty(row2rowofdam(i))
       for j = 1:(i-1)
          A(j,i) = 0; 
       end
       A(i,i) = 1;
       continue
   end
   
   if isempty(row2rowofsire(i)) && not(isempty(row2dam(i)))
       for j = 1:(i-1)
           A(j,i) = A(j,row2rowofdam(i));
           A(i,j) = A(j,row2rowofdam(i));
       end
       A(i,i) = 1;
       continue
   end
   
   if isempty(row2rowofdam(i)) && not(isempty(row2rowofsire(i)))
       for j=1:(i-1)
           A(j,i) = A(j,row2rowofsire(i));
           A(i,j) = A(j,row2rowofsire(i));
       end
       A(i,i) = 1;
       continue
   end
   
   for j=1:(i-1)
       A(j,i) = 0.5.*(A(j,row2rowofsire(i)) + A(j,row2rowofdam(i)));
       A(i,j) = 0.5.*(A(j,row2rowofsire(i)) + A(j,row2rowofdam(i)));
   end
   A(i,i) = 1 + 0.5.*A(row2rowofsire(i), row2rowofdam(i));
   continue
     
end

disp('Creating the additive relationship matrix... Done!')
toc
disp(' ')

%% Perform BLUP
tic
disp(' ')
disp('Solving the mixed-model equations')

% Extract the observed data
Y = cell2mat(data(:, 5));

% The identity matrix for random effects
Z = eye(numanimals, numanimals);

% Remove items where there is no data
nullobs = find(isnan(Y));
Z(nullobs, nullobs) = 0;

% Calculate heritability
lambda = (1-h2)/h2;

% Use the mixed-model equations
solutions = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]\[X'*Y; Z'*Y];

% Find the accuracies
diaginv = diag(inv([X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]));
reliability = 1 - diaginv.*lambda;

disp('Solving the mixed-model equations... Done!')
toc
disp(' ')

%% Output the results
tic
disp(' ')
disp('Saving results...')

% Find how many traits we found BLUE for
numgroups = numgroups - 1;

% Start printing results to output
fileID = fopen([savepath savename], 'w');
fprintf(fileID, 'beefblup Results Report\n');
fprintf(fileID, 'Produced using beefblup for MATLAB (');
fprintf(fileID, '%s', 'https://github.com/millironx/beefblup');
fprintf(fileID, ')\n\n');
fprintf(fileID, 'Input:\t');
fprintf(fileID, '%s', fullname);
fprintf(fileID, '\nAnalysis performed:\t');
fprintf(fileID, date);
fprintf(fileID, '\nTrait examined:\t');
fprintf(fileID, [headers{5}]);
fprintf(fileID, '\n\n');

% Print base population stats
fprintf(fileID, 'Base Population:\n');
for i = 1:length(numgroups)
   fprintf(fileID, '\t');
   fprintf(fileID, [headers{i+5}]);
   fprintf(fileID, ':\t');
   fprintf(fileID, [normal{i}]);
   fprintf(fileID, '\n');
end
fprintf(fileID, '\tMean ');
fprintf(fileID, [headers{5}]);
fprintf(fileID, ':\t');
fprintf(fileID, num2str(solutions(1)));
fprintf(fileID, '\n\n');

I = 2;

% Contemporary group adjustments
fprintf(fileID, 'Contemporary Group Effects:\n');
for i = 1:length(numgroups)
   fprintf(fileID, '\t');
   fprintf(fileID, [headers{i+5}]);
   fprintf(fileID, '\tEffect\tReliability\n');
   for j = 1:numgroups(i)
      fprintf(fileID, '\t');
      fprintf(fileID, [adjustedtraits{I-1}]);
      fprintf(fileID, '\t');
      fprintf(fileID, num2str(solutions(I)));
      fprintf(fileID, '\t');
      fprintf(fileID, num2str(reliability(I)));
      fprintf(fileID, '\n');
      
      I = I + 1;
   end
   fprintf(fileID, '\n');
end
fprintf(fileID, '\n');

% Expected breeding values
fprintf(fileID, 'Expected Breeding Values:\n');
fprintf(fileID, '\tID\tEBV\tReliability\n');
for i = 1:numanimals
   fprintf(fileID, '\t');
   fprintf(fileID, [data{i,1}]);
   fprintf(fileID, '\t');
   fprintf(fileID, num2str(solutions(i+I-1)));
   fprintf(fileID, '\t');
   fprintf(fileID, num2str(reliability(i+I-1)));
   fprintf(fileID, '\n');
end

fprintf(fileID, '\n - END REPORT -');
fclose(fileID);

disp('Saving results... Done!')
toc
disp(' ')