Merge pull request #39 from sysbiolux/v1.3_Refactor

V1.3 refactor

Author: sdelandtsheer

FALCON/DriverFalcon.m

@@ -9,9 +9,9 @@
 %     |/       |/     \|(_______/(_______/(_______)|/    )_)   %
 %                                                              %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%                   Current version: v1.2                      %
-%                      (July 2019)                         %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%                                              
+%                   Current version: v1.3                      %
+%                      (July 2020)                             %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %    CONTEXTUALIZATION OF DBN MODELS OF BIOLOGICAL NETWORKS    %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %  Sébastien De Landtsheer: sebastien.delandtsheer@uni.lu      %
@@ -22,161 +22,90 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % cite: De Landtsheer et al., 2017, Bioinformatics, 33, 3431–6 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 clc, clear all % clear screen and workspace 
 
-
-% ===========================
-% Define optimization options
-% ===========================
-optRound = 4; % Number of optimisation round
-MaxFunEvals = 3000; % Number of maximal function being evaluated (3000 = default)
-MaxIter = 3000; % Number of maximal iteration being evaluated (3000 = default)
-Parallelisation = 1; % Use multiple cores for optimisation? (0=no, 1=yes)
-HLbound = 0.5; % Qualitative threshold between high and low inputs
-InitIC = 2; % Initialise parameters' distribution (1=uniform, 2=normal, 3=zeros)
-ObjFunction = 'weighted'; % Either 'unweighted' or 'weighted'
-
-% Define plotting and saving (0=no, 1=yes)
-PlotFitEvolution    = 1; % Graph of optimise fitting cost over iteration
-PlotFitSummary      = 1; % Graph of state values at steady-state versus measurements (all in 1)
-PlotFitIndividual   = 0; % Graph of state values at steady-state versus measurements (individual)
-PlotHeatmapCost     = 1; % Heatmaps of optimal costs for each output for each condition absolute cost
-PlotStateSummary    = 1; % Graph of only state values at steady-sate (all in 1)
-PlotStateEvolution  = 1; % Graph of state values evolution over the course of the simulation (two graphs)
-PlotBiograph        = 0; % Graph of network topology, nodes activities, and optimised parameters
-PlotAllBiographs    = 0; % (Only for machines with strong GPUs) Plot all Biographs above
-
-% Additional analyses after the optimisation with the default setting (0=no, 1=yes)
-Resampling_Analysis = 1; % Resampling of experimental data and re-optimise
-NDatasets           = 50;% Number of artificial datasets from which to resample.
-
-LPSA_Analysis       = 1; % Local parameter sensitivity analysis
-Fast_Option         = 0; % Performing faster LPSA by stopping if fitting costs go over a set threshold value
-optRound_LPSA       = 5; % Number of optimizations for each perturbed datapoint
-LPSA_Increments     = 3; % Number of increments for LPSA. Increase for finer resolution
-
-KO_Analysis         = 1; % Parameter knock-out analysis
-KO_Nodes_Analysis   = 1; % Node knock-out analysis
-optRound_KO         = 5; % Number of optimizations for each KO datapoint. 
-KO_Nodes_fast       = 1; % 
-
-KO_Nodes_Analysis_eff = 1; % test different KO efficencies on each node and analyse the entire network based on this information
-efficiency_range = 0:0.1:1; % indicate the different KO efficencies you want to test (vector from 0 to 1)
-
 % Model and measurement files
 InputFile = 'PDGF_model.xlsx'; %for xls
-% InputFile='PDGF_model.sif'; %for sif
 MeasFile = 'PDGF_meas.xlsx'; %for xls
-% MeasFile={'PDGF_meas_in.csv';'PDGF_meas_out.csv';'PDGF_meas_err.csv'}; %for csv
 
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-% Creates a save folder
-SaveFolderName = ['Results_' datestr(now)]; FinalFolderName=strrep(SaveFolderName, ':', '.'); 
-mkdir(FinalFolderName) % Automatically generate a folder for saving
+% InputFile='PDGF_model.sif'; %for sif
+% MeasFile={'PDGF_meas_in.csv';'PDGF_meas_out.csv';'PDGF_meas_err.csv'}; %for csv
 
 % Builds a FALCON model for optimisation
-estim = FalconMakeModel(InputFile, MeasFile, HLbound); %make the model
+estim = FalconMakeModel(InputFile, MeasFile); %make the model
 
-% Defines optimisation options
-estim.options = optimoptions('fmincon', 'TolCon', 1e-6, 'TolFun', 1e-6, 'TolX', 1e-10, 'MaxFunEvals', MaxFunEvals, 'MaxIter', MaxIter); % Default setting
-estim.SSthresh = eps*100 ; estim.ObjFunction = ObjFunction;
-if InitIC == 1, IC_Dist = 'uniform'; elseif InitIC == 2, IC_Dist = 'normal'; elseif InitIC == 3, IC_Dist = 'scratch'; else,    error('Please choose the initial parameter distribution'), end
+%%%%
+% modify optimization parameters here
+%%%%
 
 % Optimization
-toc_all = []; x_all = []; fval_all = [];
-
-if Parallelisation
-    parfor counter = 1:optRound %'parfor' will run the parallel computing toolbox
-        tic, k = FalconIC(estim, IC_Dist); %initial conditions
-        [xval, fval] = FalconObjFun(estim, k); %objective function
-        toc_all = [toc_all; toc]; x_all = [x_all; xval]; fval_all = [fval_all; fval];
-    end
-
-else
-    for counter = 1:optRound %'parfor' will run the parallel computing toolbox
-        tic, k = FalconIC(estim, IC_Dist); %initial conditions
-        [xval, fval] = FalconObjFun(estim, k); %objective function
-        toc_all = [toc_all; toc]; x_all = [x_all; xval]; fval_all = [fval_all; fval];
-    end
-    close(h);
-end
-
-fxt_all = [fval_all x_all toc_all];
-beep; pause(0.5); beep;
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%Retrieving the results
-
-[bestx, meanx, stdx, estim] = FalconResults(estim, fxt_all, estim.param_vector, FinalFolderName);
+estim = FalconOptimize(estim);
 
 %%% Re-simulate results based on the best optimised parameter set
-[MeanStateValueAll, StdStateValueAll, MeanCostAll, StdCostAll, estim] = FalconSimul(estim, bestx, [PlotFitSummary PlotFitIndividual PlotHeatmapCost PlotStateSummary PlotStateEvolution], FinalFolderName);
+[estim, StateValues, MSE] = FalconSimul(estim);
 
-save([FinalFolderName filesep 'OptimizedModel'])
+save([estim.FinalFolderName filesep 'OptimizedModel'])
 
 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%% Analyzing the evolution of fitting cost
-if PlotFitEvolution
-  estim = FalconFitEvol(estim, IC_Dist, FinalFolderName);
+if estim.PlotFitEvolution
+  estim = FalconFitEvol(estim);
 end
 
 %%% Displayed optimised network with weights
-if PlotBiograph
-    FalconShowNetwork(estim, PlotAllBiographs, FinalFolderName)
+if estim.PlotBiograph
+    FalconShowNetwork(estim)
 end
 
 %%% Obtaining robust estimates for the parameters by resampling
-if Resampling_Analysis
-    optRound_Sampling = optRound;
-    CV_cutoff = 10; % Percent cut-off for large coefficient of variation
-    [~,~,estim] = FalconResample(estim, bestx, NDatasets, 3, CV_cutoff, FinalFolderName);
+if estim.Resampling_Analysis
+    estim = FalconResample(estim);
 end
 
 %%% Sensitivity analysis
-if LPSA_Analysis 
-    if Fast_Option, IsFast = 'fast'; else, IsFast = 'slow'; end
-    Estimated_Time_LPSA = mean(fxt_all(:, end)) * optRound_LPSA * LPSA_Increments * 3 * length(estim.param_vector);
-    disp(['Estimated Time for LPSA analysis (fast): ' num2str(Estimated_Time_LPSA) ' seconds']); beep; pause(3); beep; 
-    [~, estim] = FalconLPSA(estim, bestx, MeasFile, HLbound, optRound_LPSA, LPSA_Increments, IsFast, Parallelisation, FinalFolderName);
+if estim.LPSA_Analysis 
+    estim = FalconLPSA(estim);
 end
 
 %%% Knock-out analysis
-if KO_Analysis
-    Estimated_Time_KO = mean(fxt_all(:, end)) * optRound_KO * length(estim.param_vector);
-    disp(['Estimated Time for KO analysis: ' num2str(Estimated_Time_KO) ' seconds']); beep; pause(3); beep; 
-    estim = FalconKO(estim, fxt_all, HLbound, optRound_KO, Parallelisation, FinalFolderName);
+if estim.KO_Analysis
+    estim = FalconKO(estim);
 end
 
 %%% Nodes Knock-out analysis
-if KO_Nodes_Analysis
-    Estimated_Time_KO = mean(fxt_all(:, end)) * optRound_KO * (length(estim.state_names) - length(estim.Input_idx(1,:)));
-    disp(['Estimated Time for KO analysis: ' num2str(Estimated_Time_KO) ' seconds']); beep; pause(3); beep; 
-    estim = FalconKONodes(estim, fxt_all, HLbound, optRound_KO, Parallelisation, FinalFolderName);
-end
-
-%%% Nodes Knock-out efficency analysis
-if KO_Nodes_Analysis_eff
-    Estimated_Time_KO = mean(fxt_all(:,end)) * numel(efficiency_range) * optRound_KO * (length(estim.state_names)-length(estim.Input_idx(1,:)));
-    disp(['Estimated Time for KO analysis: ' num2str(Estimated_Time_KO) ' seconds']); beep; pause(3); beep;
-    estim=FalconKONodes_eff(estim, fxt_all, efficiency_range, HLbound, optRound_KO, FinalFolderName);    
-
+if estim.KO_Nodes_Analysis
+    estim = FalconKONodes(estim);
 end
 
 %%% Fast KO
-if KO_Nodes_fast
-    estim = FalconKONodes_fast(estim, fxt_all, HLbound, optRound_KO, Parallelisation, FinalFolderName);  
+if estim.KO_Nodes_fast
+    estim = FalconKONodes_fast(estim);  
 end
+% 
+% %%% Nodes Knock-out efficency analysis
+% if estim.KO_Nodes_Analysis_eff
+%     estim=FalconKONodes_eff(estim, fxt_all, efficiency_range, HLbound, optRound_KO, FinalFolderName);    
+% 
+% end
 
 %%% Discriminate fast effects from long-term effects
 X = estim.Results.KnockOutNodes.BIC_values;
 Y = estim.Results.KnockOutNodesFast.BIC_values;
-M1 = min(X,Y); M2 = min(X,Y);
 figure, plot(X,Y,'.k', 'MarkerSize', 15), hold on
+M1 = min(X,Y); M2 = min(X,Y);
 plot([M1, M2], [M1, M2], '-k');
+Nodes = estim.state_names;
+Nodes(estim.Input_idx(1, :)) = [];
+text(X(1), Y(1), 'base')
+for tx = 1:length(X)-1
+    text(X(tx+1), Y(tx+1), Nodes(tx))
+end
+xlabel('BIC for re-optimized model')
+ylabel('BIC for non-adapted model')
+
+
 
 
 % === End of the script === %