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netZooM 0.5.5 (#146)
* update test suite (#140) * update test suite * update test suite * update test suite * update test suite * update test suite * update test suite * update test suite * update test suite * update test suite * update test suite * update tests * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update actions * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update ci without spider * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update workflow * update actions * update github * update github * update github * update github * update github * update github * update github * fix spider test * update test data with annotation in lioness * changed test data lioness * update test data * update test data * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * separte process functions * removed macos from tests * fix savepairs (#142) * fix savepairs * fix savepairs * add eye in PPI (#145) * add eye in PPI * update tfcoop * add python gt data * add labels to panda test * expand test data
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‎netZooM/tools/NormalizeNetwork.m

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% Outputs:
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% normMat: normalized adjacency matrix
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% Author(s):
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% Kimberley Glass
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% Kimberly Glass
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%compute means and stds
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mu2 = mean(X,2);

‎netZooM/tools/processData.m

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%Initialize result
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RegNet = zeros(NumTFs,NumGenes);
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Exp = zeros(NumGenes,NumConditions);
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TFCoop = zeros(NumTFs,NumTFs);
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TFCoop = eye(NumTFs);
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%Populate result
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%Gene expression
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[ig,locg]= ismember(GeneNamesExp,GeneNames);

‎tests/testPanda.m

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initTestSuite;
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end
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function testPandaPantest()
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% Tell if this is Octave (Unit tests) or Matlab
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isOctave = exist('OCTAVE_VERSION', 'builtin') ~= 0;
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% Load statistics package from Octave
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if isOctave
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%we need the nan package because it has a fast implementation of corrcoeff
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%pkg load statistics
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pkg load nan;
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return % readtable is not available in octave
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end
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% Set Program Parameters
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exp_file = 'tests/test_data/pantests/expression.txt';
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motif_file = 'tests/test_data/pantests/motif.txt';
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ppi_file = 'tests/test_data/pantests/ppi.txt';
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panda_out = ''; % optional, leave empty if file output is not required
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save_temp = ''; % optional, leave empty if temp data files will not be needed afterward
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lib_path = '../netZooM'; % path to the folder of PANDA source code
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alpha = 0.1;
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save_pairs = 0;%saving in .pairs format
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modeProcess= 'legacy';
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% Add path
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addpath(genpath(fullfile(pwd,'tests')));
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% Call Panda
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tic;[AgNet,TFNames,GeneNames] = panda_run(lib_path,exp_file, motif_file, ppi_file, panda_out,...
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save_temp, alpha, save_pairs, modeProcess);toc;
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ExpTbl = array2table(AgNet,'RowNames',TFNames,'VariableNames',GeneNames');
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% Load the expected result
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filename = 'panda_pantests_gt_matlab.csv';
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ExpAgNet = readtable(filename,'ReadVariableNames',1,'ReadRowNames',1,'PreserveVariableNames',1);
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% Compare the outputs
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tolMat =1e-6;
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deltaMat=max(max(abs(ExpTbl{:,:}-ExpAgNet{:,:})));
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assertTrue( deltaMat < tolMat );
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end
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function testPandaPythonData()
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% Tell if this is Octave (Unit tests) or Matlab
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isOctave = exist('OCTAVE_VERSION', 'builtin') ~= 0;
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% Load statistics package from Octave
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if isOctave
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%we need the nan package because it has a fast implementation of corrcoeff
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%pkg load statistics
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pkg load nan;
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return % readtable is not available in octave
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end
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% Set Program Parameters
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exp_file = 'tests/test_data/panda/ToyExpressionData.txt';
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motif_file = 'tests/test_data/panda/ToyMotifData.txt';
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ppi_file = 'tests/test_data/panda/ToyPPIData.txt';
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panda_out = ''; % optional, leave empty if file output is not required
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save_temp = ''; % optional, leave empty if temp data files will not be needed afterward
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lib_path = '../netZooM'; % path to the folder of PANDA source code
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alpha = 0.1;
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save_pairs = 0;%saving in .pairs format
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modeProcess= 'legacy';
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% Add path
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addpath(genpath(fullfile(pwd,'tests')));
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% Call Panda
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tic;[AgNet,TFNames,GeneNames] = panda_run(lib_path,exp_file, motif_file, ppi_file, panda_out,...
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save_temp, alpha, save_pairs, modeProcess);toc;
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ExpTbl = array2table(AgNet,'RowNames',TFNames,'VariableNames',GeneNames');
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% Load the expected result
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filename = 'panda_gt_matlab.csv';
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ExpAgNet = readtable(filename,'ReadVariableNames',1,'ReadRowNames',1,'PreserveVariableNames',1);
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% Compare the outputs
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tolMat =1e-6;
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deltaMat=max(max(abs(ExpTbl{:,:}-ExpAgNet{:,:})));
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assertTrue( deltaMat < tolMat );
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end
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function testPandaSimple()
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% Tell if this is Octave (Unit tests) or Matlab
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isOctave = exist('OCTAVE_VERSION', 'builtin') ~= 0;

‎tests/test_data/panda/ToyExpressionData.txt

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