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hplc-gra-redsum-qsrr-L.m.bak
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hplc-gra-redsum-qsrr-L.m.bak
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%% The data and codes for:
% Title: Toward the general mechanistic model of liquid chromatographic retention
% Authors: Agnieszka Kamedulska, £ukasz Kubik, Julia Jacyna, Wiktoria Struck-Lewicka, Micha³ Markuszewski, Pawe³ Wiczling
% Adress: Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdañsk, Gen. J. Hallera 107, 80-416 Gdañsk, Poland
% Data: 22.03.2022
%% Load data
clear all
data = readtable('Data\1-X_Bridge_Shield_C18_5cm.csv');
data.Mod = categorical(data.Mod);
[~,~,j]=unique(data.Mod); data.Mod2=2-j; % MeOH = 1, ACN = 2
dataNames = readtable('Data\4-compounds-names.csv');
dataACD = readtable('Data\2-ACD-pKas-logP.csv');
cov.pKaslit = dataACD{:,3:7}; % pKa values as predicted by ACD
cov.pKasliterror = dataACD{:,25:29}; % pKa error as predicted by ACD
cov.chargesA = abs(dataACD{:,13:18}); % number of ionized groups (anions)
cov.chargesB = abs(dataACD{:,19:24}); % number of ionized groups (cations)
cov.charges = cov.chargesA+cov.chargesB; % absolute charge
cov.groupsA = diff(cov.chargesA,1,2); % acidic group
cov.groupsB = -diff(cov.chargesB,1,2); % basic group
cov.R = sum(cov.pKaslit<14,2); % number of dissociation steps
cov.logP = dataACD.logP; % logP
%% Load checkmol data
functional_groups = readtable('Data\6-checkmol-functional-groups.csv');
functional_groups_names = readtable('Data\Legend-checkmol-functional-group-names.csv');
functional_groups=functional_groups(:,2:end);
% combine nr of caroboxylic acid and carboxyalic acid salt functional groups
functional_groups{:,76}=functional_groups{:,76}+functional_groups{:,77};
functional_groups{functional_groups{:,202}>5.5,202} = 6; % heterocyclic compounds with more than 6 heterocycles are treated as if they have six
% exclude functional groups that repeat itself (some groups are nested)
idx_excluded = [1 2 3 6 27 28 37 47 48 51 55 61 62 67 73 74 75 77 80 91 99 109 116 117 121 125 129 142 153 154 160 161 168 173 178 181 182 186 187 191 196 199:204];
writetable(functional_groups_names(idx_excluded,:),'Tables/functional_groups_excluded.csv','Delimiter',',','QuoteStrings',false)
functional_groups_names(idx_excluded,:) = []; functional_groups(:,idx_excluded) = []; clear idx_excluded
% exclude functional groups not present on any analyte from the dataset
idx_not_present = find(sum(functional_groups{:,:})'==0);
writetable(functional_groups_names(idx_not_present,:),'Tables/functional_groups_not_present.csv','Delimiter',',','QuoteStrings',false)
functional_groups_names(idx_not_present,:) = []; functional_groups(:,idx_not_present) = []; clear idx_not_present
%% Filter Data
% Remove measurments with low score:
data(data.Score<95,:)=[];
% Remove analytes that have less than 42 measurments collected.
% There is (9+9+3)*4 = 84 measurements in total
[k,i,j]=unique(data.METID);
[cnt_uniquej, uniquej] = hist(j,unique(j));
idx = uniquej(cnt_uniquej<42);
data(ismember(j,idx),:)=[];
clear k i j cnt_uniquej uniquej idx
% Select measurment with the highest score (if several)
data.METEXPID = data.METID.*100+data.EXPID;
data = sortrows(data,{'METID','METEXPID','Score'},{'ascend','ascend','ascend'});
[k,i,j]=unique(data.METEXPID,'last');
data = data(i,:);
clear i j k
% Remove dilevalol - it's repeated in the dataset
data(data.METID==72,:)=[];
%% Prepare data. Select analytes with max two dissociation steps
[~,i1,j]=unique(data.METID,'first');
data = data(cov.R(data.METID)<=2,:);
dataACD = dataACD(unique(data.METID),:);
dataNames = dataNames(unique(data.METID),:);
cov.chargesA = cov.chargesA(unique(data.METID),:);
cov.chargesB = cov.chargesB(unique(data.METID),:);
cov.charges = cov.charges(unique(data.METID),:);
cov.groupsA = cov.groupsA(unique(data.METID),:);
cov.groupsB = cov.groupsB(unique(data.METID),:);
cov.pKaslit = cov.pKaslit(unique(data.METID),:);
cov.pKasliterror = cov.pKasliterror(unique(data.METID),:);
cov.R = cov.R(unique(data.METID));
cov.logP = cov.logP(unique(data.METID),:);
functional_groups=functional_groups(unique(data.METID),:);
clear i1 j
%
cov.maxR = max(cov.R);
cov.charges = cov.charges(:,1:cov.maxR+1);
cov.chargesA = cov.chargesA(:,1:cov.maxR+1);
cov.chargesB = cov.chargesB(:,1:cov.maxR+1);
cov.pKaslit = cov.pKaslit(:,1:cov.maxR);
cov.groupsA = cov.groupsA(:,1:cov.maxR);
cov.groupsB = cov.groupsB(:,1:cov.maxR);
cov.pKasliterror = cov.pKasliterror(:,1:cov.maxR);
% exclude functional groups not present on any analyte from the dataset
idx_not_present = find(sum(functional_groups{:,:})'==0);
writetable(functional_groups_names(idx_not_present,:),'Tables/functional_groups_not_present_2.csv','Delimiter',',','QuoteStrings',false)
functional_groups_names(idx_not_present,:) = []; functional_groups(:,idx_not_present) = []; clear idx_not_present
%% Functional groups
[SortedSum,I] = sort(sum(functional_groups{:,:}>0.5));
figure('Color',[1 1 1])
subplot(1,2,1)
plot(1:1:30,SortedSum([1:1:30]),'-o')
xlabel('Functional group')
ylabel(' Number of analytes having at least one functional group of a given type')
view(90,90)
set(gca,'Xtick',[1:1:30],'XTickLabelRotation',0,'XTickLabel',functional_groups_names{I([1:1:30]),2})
set(gca,'Yscale','lin','FontSize',8)
subplot(1,2,2)
plot(31:1:60,SortedSum([31:1:60]),'-o')
view(90,90)
set(gca,'Xtick',[31:1:60],'XTickLabelRotation',0,'XTickLabel',functional_groups_names{I([31:1:60]),2})
set(gca,'Yscale','log','FontSize',8)
clear I SortedSum
% Figure S3. Functional groups identified by Checkmol. Figures show the number of analytes having at least one functional group of a given type
savefig('Figures/FunctionalGroups.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/FunctionalGroups.tif
%% Plot raw data (6 selected analytes)
uMETID=unique(data.METID);
uMETID_sample = [8 9 17 33 58 180]; % uMETID_sample = uMETID;
for i= 1:length(uMETID_sample);
Names = data.Name(data.METID==uMETID_sample(i));
plot_data(data,uMETID_sample(i))
annotation(gcf,'textbox',...
[0.382142857142856 0.959328318066538 0.269642849639058 0.0369357038212866],...
'String',Names(1),...
'HorizontalAlignment','center',...
'LineStyle','none');
h2 = findall(0,'type','axes'); set(h2,'ylim', [0 max(max(cell2mat(get(h2,'ylim'))))]);
% Figure S4. Raw data for 6 selected analytes.
savefig(['Figures/Individual/RawData' Names{1} '.fig'])
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print(gcf,['Figures/Individual/RawData' Names{1} '.tiff'],'-dtiff','-r300')
close(gcf)
end
clear uMETID Names h2 i ktore uMETID_sample
%% Plot raw data (al analytes)
uMETID=unique(data.METID);
figure('Color',[1 1 1]);
for i=1:1:length(uMETID)
plot_data(data,uMETID(i))
end
h1 = findobj(gcf,'Type', 'line'); set(h1,'LineStyle','-')
h2 = findall(0,'type','axes'); set(h2,'ylim', [0 300])
% save
% Figure S2. Raw data. Lines connect measurements obtained for a particular analyte.
savefig('Figures/RawData.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/RawData.tif
clear uMETID h1 h2 i
%% Initialize variables and parameters
nObs = length(data.METID);
nAnalytes = length(unique(data.METID));
npH = length(unique(data.pH));
[~,i1,j]=unique(data.METID,'first');
[~,~,pHid]=unique(data.pH,'first');
% steps: MeOH (4-step aproximation), ACN (10-step aproximation)
datastruct = struct(...
'nAnalytes', nAnalytes, ...
'nObs',nObs, ...
'npH',npH, ...
'analyte',j,...
'pHid',pHid,...
'steps',4.*(1-data.Mod2) + 10.*(data.Mod2),...
'hplcparam',[data.tg data.td data.to data.te data.fio data.fik data.Mod2+1 data.pHo data.alpha1 data.alpha2 (data.Temp-25)/10],...
'mod', data.Mod2+1, ...
'logPobs',cov.logP, ...
'maxR',cov.maxR,...
'R',cov.R,...
'pKaslit',cov.pKaslit, ...
'pKasliterror',cov.pKasliterror, ...
'groupsA',cov.groupsA, ...
'groupsB',cov.groupsB, ...
'chargesA',cov.chargesA,...
'chargesB',cov.chargesB,...
'K', size(functional_groups,2),...
'nrfungroups',functional_groups{:,:},...
'trobs', data.RT);
clear i1 j expid npH pHid
%% Initialize
clear init0
% Initialize the values for each variable in each chain
for i=1:4
S.logkwHat = normrnd(2.2,2,1);
S.S1mHat = normrnd(4,1,1) ;
S.S1aHat = normrnd(5,1,1) ;
S.dlogkwHat = normrnd([-1 -1],0.125,1,2) ;
S.dSmHat = normrnd([0 0],0.5,1,2) ;
S.dSaHat = normrnd([0 0],0.5,1,2) ;
S.S2mHat = lognrnd(log(0.2),0.05,1,1) ;
S.S2aHat = lognrnd(log(2),0.05,1,1) ;
S.beta = normrnd([0.75 0.5 0.5],0.125,1,3) ;
S.alphaAHat = normrnd([2 2],0.2,1,2) ;
S.alphaBHat = normrnd(-[1 1],0.2,1,2) ;
S.dlogkTHat = normrnd(-0.087,0.022,1, 1);
S.omegadlogkT = lognrnd(log(0.022),0.2,1, 1);
S.apH = normrnd(0,0.1,1,2);
S.sigma = lognrnd(log(0.2),0.2,1, datastruct.nAnalytes);
S.msigma = lognrnd(log(0.2),0.2,1, 1);
S.ssigma = lognrnd(log(0.5),0.2,1, 1);
S.omega = [1 1 1] .* exp(normrnd(0, 0.5, 1, 3));
S.rho1 = [1 0.75 0.75
0.75 1 0.75
0.75 0.75 1];
S.L2 = [1 0
0.75 0.6614];
S.kappa = [0.25 0.25 0.25] .* exp(normrnd(0, 0.2, 1, 3));
S.tau = [0.5 0.5] .* exp(normrnd(0, 0.2, 1, 2));
S.pilogkw = zeros(1,datastruct.K);
S.piS1m = zeros(1,datastruct.K);
S.piS1a = zeros(1,datastruct.K);
S.sdpi = [0.1 0.1 0.1] .* exp(normrnd(0, 0.1, 1, 3));
S.param = [2+1.*datastruct.logPobs 4*ones(datastruct.nAnalytes,1)+0.5.*datastruct.logPobs 5*ones(datastruct.nAnalytes,1)+0.5.*datastruct.logPobs];
S.dlogkwA = -1.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dlogkwB = -1.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dSmA = 0.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dSmB = 0.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dSaA = 0.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dSaB = 0.*ones(datastruct.nAnalytes,datastruct.maxR+1);
S.dlogkT = normrnd(-0.0868,0.0217, 1, datastruct.nAnalytes);
S.pKaw = datastruct.pKaslit;
S.etaStd1 =zeros(2,datastruct.nAnalytes);
S.etaStd2 =zeros(2,datastruct.nAnalytes);
init0(i) = S;
end
clear S i i1 j kaHat kwHat nAnalytes nObs fi nExp
%% Use Stan for optimization
setenv('STAN_NUM_THREADS','6')
fprintf( 'Running Stan...\n' );
fito= stan('file','hplc-gra-redsum-qsrr-L.stan','data', datastruct,'method','optimize', ...
'working_dir','Tmpstan','verbose', logical(1),'init',init0(1),'iter',1000,'warmup',1000, ...
'stan_home', 'C:\Users\biofarm\Documents\.cmdstanr\cmdstan-2.25.0');
fito.block()
save('fito.mat', 'fito','-v7.3')
%% Use Stan for prior predcitve check
% set environmentla variables
setenv('STAN_NUM_THREADS','6')
fprintf( 'Running Stan...\n' );
fitp= stan('file','hplc-gra-redsum-qsrr-L-priors.stan','data', datastruct, 'verbose', logical(1), ...
'working_dir','Tmpstan','iter',1000,'warmup',100,'chains',4,'init',init0, ...
'stan_home', 'C:\Users\biofarm\Documents\.cmdstanr\cmdstan-2.25.0');
fitp.block();
save('fitp.mat', 'fitp','-v7.3')
%% Use Stan for sampling
% set environmentla variables
setenv('STAN_NUM_THREADS','6')
% set initial values for param based on opitmization
for i =1:4; init0(i).param = fito.sim.samples.param; end
fprintf( 'Running Stan...\n' );
fit= stan('file','hplc-gra-redsum-qsrr-L.stan','data', datastruct, 'verbose', logical(1), ...
'working_dir','Tmpstan','iter',1000,'warmup',1000,'chains',4,'init',init0, ...
'stan_home', 'C:\Users\biofarm\Documents\.cmdstanr\cmdstan-2.25.0');
fit.block();
%% Summary of model parameters. Save to file
diary hplc-gra-redsum-qsrr-L.txt
fit.print();
diary off
save hplc-gra-redsum-qsrr-L.mat '-v7.3'
%% Extract samples
samples = extract_stan_samples
save('samples-hplc-gra-redsum.mat', 'samples','-v7.3')
%% Simulate for better graphics
hplcparam_sim = readtable('Data\hplcparam_design.csv');
samples_sim = hplc_gra_sim(samples,datastruct,hplcparam_sim{:,:});
save('samples-hplc-gra-redsum-sim.mat', 'samples_sim','-v7.3')
%% Load saved data
hplcparam_sim = readtable('Data\hplcparam_design.csv');
load hplc-gra-redsum-qsrr-L.mat
load samples-hplc-gra-redsum.mat %
load samples-hplc-gra-redsum-sim.mat
%% Goodness of Fit Plots, GOF
trPred_mean = mean(samples.trPred);
trCond_mean = mean(samples.trCond);
figure('Color', [1 1 1]);
subplot(3,1,1)
hold on
gscatter(trPred_mean,datastruct.trobs',datastruct.analyte)
xlabel('Population predicted t_{R,z}')
ylabel('Observed t_{R,z}')
plot(xlim,xlim,':')
xlim([0 350]);
ylim([0 350]);
legend off
subplot(3,1,2)
hold on
gscatter(trCond_mean,datastruct.trobs',datastruct.analyte)
plot(xlim,xlim,':')
xlabel('Individual Predicted t_{R,z}')
ylabel('Observed t_{R,z}')
legend off
xlim([0 350]);
ylim([0 350]);
subplot(3,1,3)
hold on
gscatter(data.EXPID ,datastruct.trobs'-trCond_mean,datastruct.analyte)
plot(xlim,[0 0],':')
xlabel('Experiment ID')
ylabel({'Residuals'})
legend off
xlim([1 84]);
% Figure 3. Goodness-of-fit plots. The observed vs. the mean population-predicted retention factors (i.e., a posteriori means of predictive distributions corresponding to the future observa-tions of a new analyte), the observed vs. the mean individual-predicted retention times (i.e., a posteriori mean of a predictive distribution conditioned on the observed data from the same analyte), and the residuals vs. experiment ID.
savefig('Figures/GOF.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/GOF.tif
clear logkCond_mean logkPred_mean
clear trCond_mean trPred_mean
%% Trace plots:
samples_np = fit.extract('permuted',false);
Param = 'S2aHat';
[~,m]=size(samples_np(1).(Param));
for i=1:min(m,10)
figure('Color', [1 1 1]);
for z=1:4
hold on
plot(samples_np(z).(Param)(:,i),'-');
xlabel('Iteration')
end
ylabel([Param '(:,' num2str(i) ')'],'fontsize',12);
end
clear samples_np Param z i n m
%% Marginal posterior and prior distributions for the population-level parameters
load fitp.mat
samplesp = fitp.extract;
h1 = figure;
Names = {'logkwHat' 1 '\theta_{logkwN}'
'S1mHat' 1 '\theta_{S1mN}'
'S1aHat' 1 '\theta_{S1aN}'
'S2mHat' 1 '\theta_{S2m}'
'S2aHat' 1 '\theta_{S2a}'
'dlogkwHat' 1 '\theta_{dlogkwA}'
'dlogkwHat' 2 '\theta_{dlogkwB}'
'dSmHat' 1 '\theta_{dSmA}'
'dSmHat' 2 '\theta_{dSmB}'
'dSaHat' 1 '\theta_{dSaA}'
'dSaHat' 2 '\theta_{dSaB}'
'dlogkTHat' 1 '\theta_{dlogkT}'
'beta' 1 '\beta_{logkwN}'
'beta' 2 '\beta_{S1mN}'
'beta' 3 '\beta_{S1aN}'
'alphaAHat' 1 '\alpha_{mA}'
'alphaAHat' 2 '\alpha_{aA}'
'alphaBHat' 1 '\alpha_{mB}'
'alphaBHat' 2 '\alpha_{aB}'
'omega' 1 '\omega_{logkwN}'
'omega' 2 '\omega_{S1mN}'
'omega' 3 '\omega_{S1aN}'
'rho1' 2 '\rho_1_{ [logkwN,S1mN]}'
'rho1' 3 '\rho_1_{ [logkwN,S1aN]}'
'rho1' 6 '\rho_1_{ [S1aN,S1mN]}'
'omegadlogkT' 1 '\omega_{dlogkT}'
'kappa' 1 '\kappa_{dlogkw}'
'kappa' 2 '\kappa_{dSm}'
'kappa' 3 '\kappa_{dSa}'
'tau' 1 '\tau_{m}'
'tau' 2 '\tau_{a}'
'rho2' 2 '\rho_2_{ [\alpham,\alphaa]}'
'apH' 1 'apH_{A}'
'apH' 2 'apH_{B}'
'msigma' 1 'm_{\sigma}'
'ssigma' 1 's_{\sigma}'}
clear Param
for i=1:size(Names,1)
temp = samplesp.(Names{i,1});
Param(:,i) = squeeze(temp(:,Names{i,2}));
end
hold on
boxplot_pwhisker(Param,{'Labels',Names(:,3)},5,95);
set(gca, 'TickLabelInterpreter', 'tex');
view(90,90)
set(gca,'FontSize',10)
set(gca,'Position', [0.2343 0.1100 0.6707 0.8150])
ylabel('Marginal prior/posterior distributions','FontSize',10)
h = findobj(gca,'Tag','Box');
for j=1:length(h)
patch(get(h(j),'XData'),get(h(j),'YData'),[0.8 0.8 0.8],'FaceAlpha',.5,'EdgeColor',[0.8 0.8 0.8]);
end
h = findobj(gca,'type','line');
set(h,'Color',[0.8 0.8 0.8])
ax1=gca;
% savefig('Figures/PriorDistribution.fig')
% set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
% print -dtiff -r300 Figures/PriorDistribution.tif
clear temp Param
% Add posterior
h2 = figure
clear Param
for i=1:size(Names,1)
temp = samples.(Names{i,1});
Param(:,i) = temp(:,Names{i,2});
end
hold on
boxplot_pwhisker(Param,{'Labels',Names(:,3)},5,95);
set(gca, 'TickLabelInterpreter', 'tex');
view(90,90)
set(gca,'FontSize',10)
set(gca,'Position', [0.2343 0.1100 0.6707 0.8150])
ylabel('Marginal prior/posterior distributions','FontSize',10)
h = findobj(gca,'Tag','Box');
for j=1:1:length(h)
patch(get(h(j),'XData'),get(h(j),'YData'),'b','FaceAlpha',.5,'EdgeColor','b');
end
h = findobj(gca,'type','line');
set(h,'Color','b')
ax2 = gca; ax2Chil = ax2.Children; % Get handles for all children from ax2
copyobj(ax2Chil, ax1);% Copy all ax2 objects to axis 1
close(h2)
set(ax1,'Ylim',[-1.5 6.5])
% Figure 1. Graphical display of the marginal posterior (blue) and prior (gray) distributions for the population-level parameters.
savefig('Figures/PosteriorDistribution.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/PosteriorDistribution.tif
%
clear temp Param Names h1 h2 h ax1 ax2 i j ax2Chil
%% Calculate individual (analyte-specific) parameters
idata.logP =cov.logP;
for j = 1:1:datastruct.nAnalytes
samples.etap(:,j,1) = samples.param(:,j, 1) - samples.miu(:,j,1); %
samples.etap(:,j,2) = samples.param(:,j, 2) - samples.miu(:,j,2); %
samples.etap(:,j,3) = samples.param(:,j, 3) - samples.miu(:,j,3); %
end
idata.etap = squeeze(mean(samples.etap)); % logkw, S1, S2
idata.param = squeeze(mean(samples.param)); % logkw, S1, S2
idata.logkwx = squeeze(mean(samples.logkwx));
idata.logkmx = squeeze(mean(samples.logkwx-samples.S1mx));
idata.logkax = squeeze(mean(samples.logkwx-samples.S1ax));
idata.pKaw = squeeze(mean(samples.pKaw));
idata.alpham = squeeze(mean(samples.alpham));
idata.alphaa = squeeze(mean(samples.alphaa));
idata.pKam=squeeze(mean(samples.pKaw+samples.alpham));
idata.pKaa = squeeze(mean(samples.pKaw+samples.alphaa));
for j = 1:1:datastruct.nAnalytes
samples.dlogkw(:,j,:) = (squeeze(samples.dlogkwA(:,j,:))) .* datastruct.chargesA(j,:) ...
+ (squeeze(samples.dlogkwB(:,j,:))) .* datastruct.chargesB(j,:);
samples.dS1m(:,j,:) = squeeze(samples.dSmA(:,j,:)) .* datastruct.chargesA(j,:) ...
+ squeeze(samples.dSmB(:,j,:)) .* datastruct.chargesB(j,:) ;
samples.dS1a(:,j,:) = ( squeeze(samples.dSaA(:,j,:))) .* datastruct.chargesA(j,:) ...
+ (squeeze(samples.dSaB(:,j,:))).* datastruct.chargesB(j,:) ;
end
samples.dlogkm = samples.dlogkw - samples.dS1m;
samples.dlogka = samples.dlogkw - samples.dS1a;
idata.dlogkw = squeeze(mean(samples.dlogkw))./cov.charges;
idata.dlogkm = squeeze(mean(samples.dlogkm))./cov.charges;
idata.dlogka = squeeze(mean(samples.dlogka))./cov.charges;
idata.dS1m = squeeze(mean(samples.dS1m))./cov.charges;
idata.dS1a = squeeze(mean(samples.dS1a))./cov.charges;
idata.chargesAB = cov.chargesB-cov.chargesA; % {-2,-1,0,1,2}
idata.groupsAB = cov.groupsB-cov.groupsA; % {-1 for Acids, 1 for Bases}
idata.isdiss = 0.*cov.charges;
for j = 1:1:datastruct.nAnalytes
idata.isdiss(j,1:cov.R(j)+1)=1;
end
clear j
%% Individual Parameters - Neutral Form
figure('Color', [1 1 1]);
xynames = {'logkwN_{i}','S1mN_{i}','S1aN_{i}','logP_i'};
gplotmatrix([idata.param(:,1) idata.param(:,2) idata.param(:,3) idata.logP],[],0*idata.logP,'kk',[],[],'on','stairs',xynames,xynames)
h=get(gcf,'children');
set(h(1),'Visible','off')
% Figure S5. Scatter plots between individual chromatographic parameters and their relationship to log P. Diagonal subplots present histograms.
savefig('Figures/IndividualParametersNeutralForm.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/IndividualParametersNeutralForm.tif
clear h xynames
%% Effect of dissociation
figure('Color', [1 1 1]);
xynames = {'dlogkw_{r,i}','dS1m_{r,i}','dS1a_{r,i}'};
ktore = idata.isdiss(:)~=0;
gplotmatrix([idata.dlogkw(ktore) idata.dS1m(ktore) idata.dS1a(ktore)],[],idata.chargesAB(ktore),'rrybb',[],[],'on','stairs',xynames,xynames)
h=get(gcf,'children');
set(h(1),'Visible','off')
% Figure S6. Scatter plots between individual chromatographic parameters describing the effect of dissociation (dlogkw, dS1m, dS1a were normalized by the absolute charge). Diagonal subplots present histograms. Colors corresponds to different charge state of analyte form (red anions, blue cations, yellow zwitterions).
savefig('Figures/IndParamEffectDiss.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/IndParamEffectDiss.tif
clear h xynames ktore
%% pKas
xynames = {'pKaw_{i,r}','pKam_{i,r}','pKaa_{i,r}','pKawlit_{i,r}'};
X=[idata.pKaw(:) idata.pKam(:) idata.pKaa(:) datastruct.pKaslit(:)]; X(X>12)=NaN; X(X<2)=NaN;
Y = idata.groupsAB(:);
[h,ax,bigax] = gplotmatrix(X(Y~=0,:),[],Y(Y~=0),'rbgkym',[],[],'on','stairs',xynames,xynames)
set(ax,'XTick',2:2:12,'YTick',2:2:12)
set(ax(1:4,1),'YTickLabel',2:2:12)
set(ax(end,:),'XTickLabel',2:2:12)
% Figure S7. Scatter plots between individual chromatographic parameters (pKas) and their relationship to literature pKa. Diagonal subplots present histograms. Red color denotes acidic group, blue color basic group.
savefig('Figures/IndParampKas.fig')
h=get(gcf,'children');
set(h(1),'Visible','off')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/IndParampKas.tif
clear h ax bigax xynames X Y
%% Sigmas
hist(mean(log(samples.sigma)))
xlabel('\sigma_i')
%% Effect of temperature
hist(mean(samples.dlogkT))
xlabel('dlogkT_i')
%% Influence of functional groups
figure('Color', [1 1 1]);
subplot(1,4,2)
hold on
boxplot_pwhisker(samples.pilogkw(:,:),{'Labels',functional_groups_names{:,2}},5,95);
plot(xlim,[0 0],':')
ylim([-1 1])
view(90,90)
set(gca,'FontSize',5)
set(gca,'Position', [0.2139 0.1100 0.2138 0.8150])
ylabel('\pi_{logkwN}','FontSize',8)
subplot(1,4,3)
hold on
boxplot_pwhisker(samples.piS1m(:,:),{'Labels',functional_groups_names{:,1}},5,95);
plot(xlim,[0 0],':')
ylim([-1 1])
view(90,90)
set(gca,'FontSize',5)
set(gca,'Position', [0.4854 0.1100 0.2178 0.8150])
ylabel('\pi_{S1mN}','FontSize',8)
subplot(1,4,4)
hold on
boxplot_pwhisker(samples.piS1a(:,:),{'Labels',functional_groups_names{:,1}},5,95);
plot(xlim,[0 0],':')
ylim([-1 1])
view(90,90)
set(gca,'FontSize',5)
set(gca,'Position', [0.7334 0.1100 0.1708 0.8150])
ylabel('\pi_{S1aN}','FontSize',8)
% Figure 2. Graphical display of the marginal posterior distribu-tions for the effects of each functional group on logkwN, S1mN, and S1aN.
savefig('Figures/FunctionalGroupEffects.fig')
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print -dtiff -r300 Figures/FunctionalGroupEffects.tif
%% Individual and population predictions (6 selected analytes)
prediction_type = 'trObsCond'; % trObsPred || trObsCond
metidx = [8 9 17 33 58 180];
idx = find(ismember(unique(data.METID),metidx));
Names = dataNames{ismember(dataNames{:,1},metidx),2};
for i=1:length(metidx);
figure('Color', [1 1 1]);
plot_data(data,metidx(i))
plot_sim(samples_sim,hplcparam_sim,idx(i),prediction_type)
annotation(gcf,'textbox',...
[0.382142857142856 0.959328318066538 0.269642849639058 0.0369357038212866],...
'String',Names(i),...
'HorizontalAlignment','center',...
'LineStyle','none');
h2 = findall(0,'type','axes'); set(h2,'ylim', [0 max(max(cell2mat(get(h2,'ylim'))))]);
savefig(['Figures/Individual/' prediction_type Names{i} '.fig'])
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print(gcf,['Figures/Individual/' prediction_type Names{i} '.tiff'],'-dtiff','-r300')
close(gcf)
end
clear h2 idx i metidx prediction_type Names
%% Individual and population predictions (all analytes)
prediction_type = 'trObsCond'; % trObsPred || trObsCond
metidx = unique(data.METID);
idx = find(ismember(unique(data.METID),metidx));
Names = dataNames{ismember(dataNames{:,1},metidx),2};
for i=1:length(metidx);
figure('Color', [1 1 1]);
plot_data(data,metidx(i))
plot_sim(samples_sim,hplcparam_sim,idx(i),prediction_type)
annotation(gcf,'textbox',...
[0.382142857142856 0.959328318066538 0.269642849639058 0.0369357038212866],...
'String',Names(i),...
'HorizontalAlignment','center',...
'LineStyle','none');
h2 = findall(0,'type','axes'); set(h2,'ylim', [0 max(max(cell2mat(get(h2,'ylim'))))]);
savefig(['Figures/All/' prediction_type Names{i} '.fig'])
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print(gcf,['Figures/All/' prediction_type Names{i} '.tiff'],'-dtiff','-r300')
close(gcf)
end
clear h2 idx i metidx prediction_type Names
%% Uncertainty chromatograms (population and individual predcitions) for expid = 47
map = colormap('lines');
figure('Color', [1 1 1]);
metidx = [8 9 17 33 58 180];
idx = find(ismember(unique(data.METID),metidx));
expid = [47];
expidx = find(hplcparam_sim.expid==expid);
subplot(2,1,1)
plot_uncertainity_chromatogram(samples_sim.trObsPred,expidx,idx)
tr = data.RT(ismember(data.METID,metidx)&data.EXPID==expid);
for i=1:length(tr); plot([tr(i) tr(i)], ylim,':','Color',map(i,:)); end
xlim([0 35])
title('Population Predictions')
subplot(2,1,2)
plot_uncertainity_chromatogram(samples_sim.trObsCond,expidx,idx)
tr = data.RT(ismember(data.METID,metidx)&data.EXPID==expid);
for i=1:length(tr); plot([tr(i) tr(i)], ylim,':','Color',map(i,:)); end
xlim([0 35])
legend1=legend(dataNames{ismember(dataNames{:,1},metidx),2})
set(legend1,...
'Position',[0.723690473363513 0.155566707459585 0.20964285996982 0.17151941480726],...
'EdgeColor',[1 1 1]);
title('Individual Predictions')
xlabel('t_R, min')
clear tr idx expid expidx metidx i map legend1
%% Limited Data predicitons. Initialize variables and parameters
metidx = [8 9 17 33 58 180];
expidx = [10 58 38];
hplcparam_sim((ismember(hplcparam_sim.expid,expidx)),:)
idx = find(ismember(unique(data.METID),metidx));
% Predictions based on selected measurments
data_red = data(ismember(data.METID,metidx),:);
data_red(~ismember(data_red.EXPID,expidx),:)=[];
% Functional group effects
pilogkw = [0.0889968428595548 0.177471413490907;0.220089668251250 0.105968524228844;-0.0771815195591502 0.160325195284994;-0.0581799102143500 0.185308852260919;-0.0201489460052250 0.179988182045063;0.0307316725699825 0.188612904909725;0.0543621082853000 0.120183861760715;0.0324279323432999 0.182588420787220;-0.0559667868367500 0.171610973282035;-0.0415813552520000 0.127933551854748;-0.0180587034066250 0.180318487129278;-0.233834863103000 0.119557887197776;0.180337220964750 0.0846757399085927;0.222922921009750 0.108777856957033;-0.000501192305000001 0.131647570428321;0.0498735976307500 0.146940875915100;0.0513917052316499 0.132389150840822;0.287699338262500 0.111017971807696;0.124327135344325 0.0609063261016972;0.125937048681750 0.173498858874232;-0.0935018665000727 0.119702270923968;0.110744323446425 0.155684606307299;0.0988604463651751 0.154538855196298;0.0143156623489500 0.102276959128834;0.172177835703000 0.132273861408408;-0.0643482931813277 0.149964201815220;-0.0329482021954501 0.151168437643277;0.423955068749999 0.0885875918043772;0.0993908985895301 0.114247980097966;0.0298201324193250 0.182464612224784;0.262131928062750 0.162515143173171;0.125264699990745 0.139539749944581;0.108892420331975 0.106826055058630;0.0343301783096001 0.0934536247436581;0.143383295807107 0.151605033204463;-0.101369205041500 0.147919735532570;-0.0913377286217774 0.104111219920434;0.114594420303100 0.100462396653018;0.137289133807375 0.172043654639451;-0.159722669718600 0.165543368574018;-0.100180180674275 0.123600934733901;-0.172452433209500 0.143307896625322;-0.151108470096749 0.161005113067004;-0.0716024258115749 0.147898507889060;-0.410842843382499 0.140738135836306;0.000433213414249995 0.175875588073256;0.0476187438239748 0.0922153601330513;0.00506268302377501 0.182678051432591;0.0826298697423501 0.167920419044898;-0.00806687702650001 0.182219831488352;0.0879922134395501 0.181998003635673;0.160473326353290 0.166060658904532;-0.00545335401175000 0.180062747952312;0.0491115158007500 0.148776460100283;-0.0790377194335501 0.152635415304690;0.0804110515920003 0.186671844754081;0.0556822471320525 0.132266474042620;0.100240845222148 0.109456712990130;-0.0393841636146501 0.187559603506411;0.0723208501620699 0.182642665955130];
piS1m=[0.0217000559264250 0.154957706487726;0.106134236236000 0.103414295235257;0.0778085607394998 0.145230648283856;0.0655436159672500 0.159787278072099;0.00522921446779999 0.160644821033471;-0.00690202034879999 0.169823560721250;0.208429806273775 0.116246517233544;-0.0118745781861125 0.159477133102472;0.100122203701200 0.154500566094926;0.0644238794738526 0.120777670402343;0.0397175303883750 0.156209567867953;-0.00329354588375000 0.124583226054949;0.197713133829750 0.0897640046925205;0.0623477881566750 0.108635663842868;-0.00735698785372498 0.133018819334084;0.0655856143230000 0.135373389640694;-0.0676902528240000 0.127087050009747;0.147878240230000 0.110581200578122;0.244202023450001 0.0675280219418342;-0.0442913221941248 0.150922383973941;0.173501527884500 0.113705291932268;-0.0365348548789000 0.138043420187343;-0.105239627925450 0.145407961486631;0.0317578123057000 0.101285112390947;-0.00892374372052501 0.119905477353245;0.0149970674375000 0.135600616114253;-0.0448266668852501 0.130958829322927;0.301479356809751 0.0906550705894951;0.0594514271803000 0.112569001623926;0.0431497501975751 0.168617953959701;0.0485574303400775 0.140023445586041;0.0769855942661501 0.128243679718009;0.145813218572550 0.107365014749321;0.0279250855683500 0.0932651788037824;0.0303524569695750 0.129411974521634;0.178533757770950 0.139406109232078;-0.0842705905559500 0.104531826953638;0.154872312716500 0.0996907059774276;0.132141557586992 0.152110361897885;-0.0405228301690000 0.157263720169405;-0.140858090681658 0.118152826876805;0.156149595732875 0.133036941759429;0.134440491507875 0.138817968012840;0.139985368892750 0.133760811454953;-0.201352129304250 0.130151112570089;0.103264498479375 0.158431454882315;0.176517505312900 0.107967439587904;-0.142850163107775 0.176517622964707;0.0640129549986750 0.150302065640594;0.0308196197489499 0.163472973013075;0.0309345101660500 0.154618187499486;0.0439905966312500 0.141494845812313;0.0335519910824000 0.160873678513828;-0.0415121552322499 0.129682894598821;0.0865000891908250 0.135453887389572;-0.0418402961457725 0.162897804074594;0.107768039835750 0.128110626150208;0.144802405712934 0.113721157394385;0.193430843130750 0.169572416239922;0.0939918425327248 0.162166456557561];
piS1a =[0.0698588119030001 0.266997955870408;-0.0144656829931750 0.148733155306423;-0.0732117244065000 0.227686995189610;-0.161391291165675 0.275901409852163;0.0338917363063001 0.271877410824601;-0.0126832724365000 0.298346663477855;0.0203286872035000 0.177806814002461;0.111895183797900 0.271521660214816;-0.0993323177924252 0.241436538993382;0.0941978176669001 0.180301450303532;-0.0231392560610000 0.274114484568687;0.462180803630000 0.188957918025456;0.596185300750001 0.126331207619497;0.0747870440726349 0.160085514634547;0.215890568380500 0.201692251523540;0.0111592813609250 0.214000122466492;-0.174233959451578 0.189218817192267;0.301355481089251 0.159306379551736;0.370040966425000 0.0916786440894036;0.0844944851305248 0.246941048748585;0.216316038603325 0.168665961619620;0.0142375086965000 0.218564066263434;0.0210094238269000 0.223810574360652;-0.112519956732200 0.140485010893325;0.147743851832600 0.182825344199810;0.301887597099050 0.213382465113893;0.0615851100933250 0.203977937016960;0.350459336138750 0.127671681824427;0.196511031060970 0.162785467231247;-0.207562572348100 0.319149603000405;-0.240331138083249 0.215819497157118;-0.710979029074999 0.204438130939904;0.0522476095115002 0.153576361682664;0.0590822581768752 0.134308875041780;-0.196431292645900 0.195638042452994;-0.100291397251750 0.204207737155929;-0.197336832944275 0.145253622132466;0.144002680948375 0.145300767608426;-0.0980488794257999 0.264017658442989;0.474931054038000 0.248261603630060;-0.0508391006226000 0.172241816537243;0.564059040740000 0.210695763630808;0.0679908603261500 0.226609867181402;-0.351798112383883 0.206893336516015;0.142693050118950 0.187933820047448;-0.247756557571000 0.262197661810480;0.538594641175001 0.145058363009612;0.589749770952500 0.303034971268726;-0.0625016822134999 0.255047107307086;0.0519043844712500 0.283839486679962;-0.116257019861325 0.274911738051457;0.0535596520790248 0.222526855386509;0.0484423938490250 0.291964169639225;0.170557791367350 0.200459681659015;-0.230579356470800 0.217900375881488;0.0139409652806000 0.267260001573277;-0.106784199815388 0.193770940997105;-0.566092862437250 0.157219504776589;-0.400852275487326 0.285199083543308;-0.112792394288325 0.274935172540649];
clear metidx expidx idx
%% Exclude unnecesary data
nObs = length(data_red.METID);
nAnalytes = length(unique(data_red.METID));
npH = length(unique(data_red.pH));
[~,i1,j]=unique(data_red.METID,'first');
[~,~,pHid]=unique(data_red.pH,'first');
% steps: MeOH (4-step aproximation), ACN (10-step aproximation)
datastruct_small = struct(...
'nAnalytes', nAnalytes, ...
'nObs',nObs, ...
'npH',npH, ...
'analyte',j,...
'pHid',pHid,...
'steps',4.*(1-data_red.Mod2) + 10.*(data_red.Mod2),...
'hplcparam',[data_red.tg data_red.td data_red.to data_red.te data_red.fio data_red.fik data_red.Mod2+1 data_red.pHo data_red.alpha1 data_red.alpha2 (data_red.Temp-25)/10],...
'mod', data_red.Mod2+1, ...
'logPobs',cov.logP(idx), ...
'maxR',cov.maxR,...
'R',cov.R(idx,:),...
'pKaslit',cov.pKaslit(idx,:), ...
'pKasliterror',cov.pKasliterror(idx,:), ...
'groupsA',cov.groupsA(idx,:), ...
'groupsB',cov.groupsB(idx,:), ...
'chargesA',cov.chargesA(idx,:),...
'chargesB',cov.chargesB(idx,:),...
'mpilogkw', pilogkw(:,1)', ...
'spilogkw', pilogkw(:,2)', ...
'mpiS1m', piS1m(:,1)', ...
'spiS1m', piS1m(:,2)', ...
'mpiS1a', piS1a(:,1)', ...
'spiS1a', piS1a(:,2)', ...
'K', size(functional_groups,2),...
'nrfungroups',functional_groups{idx,:},...
'trobs', data_red.RT, ...
'run_estimation', 1);
clear i1 j expid npH pHid nObs nAnalytes
%% Initialize
clear init0_simple
% Initialize the values for each variable in each chain
for i=1:4
S.logkwHat = normrnd(3.1543,0.0871,1);
S.S1mHat = normrnd(4.5141,0.0971,1) ;
S.S1aHat = normrnd(5.5600,0.1348,1) ;
S.dlogkwHat = normrnd([-0.7357,-0.9359],[0.0588,0.0461],1,2) ;
S.dSmHat = normrnd([0.3311,0.1098],[0.1030,0.0704],1,2) ;
S.dSaHat = normrnd([0.8910,-0.4577],[0.1057,0.0670],1,2) ;
S.S2mHat = normrnd(0.3741,0.0250,1,1) ;
S.S2aHat = normrnd(0.8194,0.0342,1,1) ;
S.beta = normrnd([0.7442,0.3553,0.3895],[0.0313,0.0393,0.0513],1,3) ;
S.alphaAHat = normrnd([1.9736,2.1454],[0.1703,0.1844],1,2) ;
S.alphaBHat = normrnd([-1.0005,-0.8940],[0.1405,0.1641],1,2) ;
S.dlogkTHat = normrnd(-0.0946,0.0026,1, 1);
S.omegadlogkT = normrnd(0.0344,0.0021,1, 1);
S.apH = normrnd([-0.0238,0.0851],[0.0010, 0.0009],1,2);
S.sigma = min(3.9,lognrnd(log(0.3671),1 ,1, datastruct_small.nAnalytes));
S.msigma = normrnd(0.3671, 0.0278,1, 1);
S.ssigma = normrnd(0.9989,0.0536,1, 1);
S.omega = [0.6150,0.6762,0.9206] .* exp(normrnd([0.0393,0.0469,0.0631], 0.5, 1, 3));
S.rho1 = [1 0.7811 0.7135
0.7811 1 0.9148
0.7135 0.9148 1];
S.L2 = [1 0
0.9408 0.3355];
S.kappa = [0.5305,0.5526,0.5409] .* exp(normrnd([0.0276,0.0447,0.0422], 0.2, 1, 3));
S.tau = [2.2561,2.5580] .* exp(normrnd([0.1644,0.1841], 0.2, 1, 2));
S.pilogkw = normrnd(pilogkw(:,1)',pilogkw(:,2)',1,datastruct_small.K);
S.piS1m = normrnd(piS1m(:,1)',piS1m(:,2)',1,datastruct_small.K);
S.piS1a = normrnd(piS1a(:,1)',piS1a(:,2)',1,datastruct_small.K);
S.sdpi = [0.1964,0.1736,0.3162] .* exp(normrnd([0.0301,0.0288,0.0387], 0.1, 1, 3));
S.param = [2+0.75.*datastruct_small.logPobs 4*ones(datastruct_small.nAnalytes,1)+0.3.*datastruct_small.logPobs 5*ones(datastruct_small.nAnalytes,1)+0.3.*datastruct_small.logPobs];
S.dlogkwA = -0.7357.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dlogkwB = -0.9359.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dSmA = 0.3311.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dSmB = 0.1098.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dSaA = 0.8910.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dSaB = -0.4577.*ones(datastruct_small.nAnalytes,datastruct_small.maxR+1);
S.dlogkT = normrnd(-0.0946,0.0217, 1, datastruct_small.nAnalytes);
S.pKaw = datastruct_small.pKaslit;
S.etaStd1 =zeros(2,datastruct_small.nAnalytes);
S.etaStd2 =zeros(2,datastruct_small.nAnalytes);
init0_simple(i) = S;
end
clear S i i1
%% Use Stan for sampling
setenv('STAN_NUM_THREADS','1')
fprintf( 'Running Stan...\n' );
fit_small= stan('file','hplc-gra-redsum-qsrr-L-fixed.stan','data', datastruct_small, 'verbose', logical(1), ...
'working_dir','Tmpstan','iter',1000,'warmup',1000,'chains',4,'init',init0_simple, ...
'stan_home', 'C:\Users\biofarm\Documents\.cmdstanr\cmdstan-2.25.0');
fit_small.block();
%% Save
diary parameters_small.txt
fit_small.print();
diary off
%% Extract samples
samples_small_10_58_38 = fit_small.extract;
samples_small_10_58_38 = hplc_gra_sim(samples_small_10_58_38,datastruct_small,hplcparam_sim{:,:});
save('samples_small_10_58_38.mat', 'samples_small_10_58_38', '-v7.3')
%%
load('samples_small_10_58_38.mat')
samples_small_sim=samples_small_10_58_38;
%% Individual predictions based on preliminary data
metidx = [8 9 17 33 58 180];
idx = find(ismember(unique(data_red.METID),metidx));
Names = dataNames{ismember(dataNames{:,1},metidx),2};
for i=1:length(metidx);
figure('Color', [1 1 1]);
plot_data(data,metidx(i))
plot_sim(samples_small_sim,hplcparam_sim,idx(i),'trObsCond')
annotation(gcf,'textbox',...
[0.382142857142856 0.959328318066538 0.269642849639058 0.0369357038212866],...
'String',Names(i),...
'HorizontalAlignment','center',...
'LineStyle','none');
h2 = findall(0,'type','axes'); set(h2,'ylim', [0 max(max(cell2mat(get(h2,'ylim'))))]);
savefig(['Figures/Individual/LimDatatrObsCond' Names{i} '.fig'])
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print(gcf,['Figures/Individual/LimDatatrObsCond' Names{i} '.tiff'],'-dtiff','-r300')
close(gcf)
end
clear h2 idx Name legend1
%% Uncertainity chromatograms (selected)
map = colormap('lines');
expid_i = hplcparam_sim.expid(hplcparam_sim.tg==30&hplcparam_sim.Temp==0);
for i=1:length(expid_i);
expid = expid_i(i);
figure('Color', [1 1 1]);
metidx = [8 9 17 33 58 180];
idx = find(ismember(unique(data.METID),metidx));
expidx = find(hplcparam_sim.expid==expid);
subplot(3,1,1)
plot_uncertainity_chromatogram(samples_sim.trObsPred,expidx,idx)
tr = data.RT(ismember(data.METID,metidx)&data.EXPID==expid);
for i=1:length(tr); plot([tr(i) tr(i)], ylim,':','Color',map(i,:)); end
xlim([0 35])
title('Population Predictions')
subplot(3,1,3)
plot_uncertainity_chromatogram(samples_sim.trObsCond,expidx,idx)
tr = data.RT(ismember(data.METID,metidx)&data.EXPID==expid);
for i=1:length(tr); plot([tr(i) tr(i)], ylim,':','Color',map(i,:)); end
xlim([0 35])
legend1=legend(dataNames{ismember(dataNames{:,1},metidx),2})
set(legend1,...
'Position',[0.723690473363513 0.155566707459585 0.20964285996982 0.17151941480726],...
'EdgeColor',[1 1 1]);
temp = hplcparam_sim(expidx,[1 7 8 11]);
if temp.Temp==0 & temp.Mod==1
xlabel(sprintf('t_R, min [tg=%d, MeOH, pH=%1.1f, 25^oC]',hplcparam_sim{expidx,[1 8]}))
end
if temp.Temp==0 & temp.Mod==2
xlabel(sprintf('t_R, min [tg=%d, ACN, pH=%1.1f, 25^oC]',hplcparam_sim{expidx,[1 8]}))
end
if temp.Temp==1 & temp.Mod==1
xlabel(sprintf('t_R, min [tg=%d, MeOH, pH=%1.1f, 35^oC]',hplcparam_sim{expidx,[1 8]}))
end
if temp.Temp==1 & temp.Mod==2
xlabel(sprintf('t_R, min [tg=%d, ACN, pH=%1.1f, 35^oC]',hplcparam_sim{expidx,[1 8]}))
end
title('Individual Predictions')
subplot(3,1,2)
idx = find(ismember(unique(data_red.METID),metidx));
expidx = find(hplcparam_sim.expid==expid);
plot_uncertainity_chromatogram(samples_small_sim.trObsCond,expidx,idx);
tr = data.RT(ismember(data.METID,metidx)&data.EXPID==expid);
for i=1:length(tr); plot([tr(i) tr(i)], ylim,':','Color',map(i,:)); end
xlim([0 35])
ylabel('Uncertainity chromatogram, pdf')
title('Limited Data Predictions')
% Figure 4 and Figure 5. Uncertainty chromatograms displaying the predictions for 6 selected analytes using different preliminary information. Each peak represents the range of analyte retention factors compati-ble with prior and preliminary data. Predictions were based on three experiments conducted at pH values of 2.5, 5.8, and 10.5 for a 30 min MeOH gradient at 25 °C. Colors correspond to different analytes that are identified in the bottom subplot. Vertical lines represent actual measurements.
savefig(['Figures/UncertainityChromatograms/UncertainityChromatogram-' num2str(expid) '.fig'])
set(gcf,'paperunits','centimeters','paperposition',[0 0 16.5 18])
print(gcf,['Figures/UncertainityChromatograms/UncertainityChromatogram-' num2str(expid) '.tiff'],'-dtiff','-r300')
close(gcf)
end
clear expid_i idx legend1 Names
%%
ver
% MATLAB Version: 9.2.0.556344 (R2017a)
% MATLAB License Number: 261217
% Operating System: Microsoft Windows 10 Pro Version 10.0 (Build 19043)
% Java Version: Java 1.7.0_60-b19 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
% ----------------------------------------------------------------------------------------------------
% MATLAB Version 9.2 (R2017a)
% Bioinformatics Toolbox Version 4.8 (R2017a)
% Curve Fitting Toolbox Version 3.5.5 (R2017a)
% Global Optimization Toolbox Version 3.4.2 (R2017a)
% MATLAB Compiler Version 6.4 (R2017a)
% MATLAB Compiler SDK Version 6.3.1 (R2017a)
% Optimization Toolbox Version 7.6 (R2017a)
% Parallel Computing Toolbox Version 6.10 (R2017a)
% SimBiology Version 5.6 (R2017a)
% Statistics and Machine Learning Toolbox Version 11.1 (R2017a)
% Symbolic Math Toolbox Version 7.2 (R2017a)