%% PCA analysis script written by Laura Ylä-Outinen

%Data organisation

X1=readtable('PCA_table.xlsx');
X1(1:36,1)=table({'hPSC_20517_MEA1and2'}); %merging MEA1 and MEA2 data as belonging to the same experiment
X2=X1(:,4:end);
X3=table2array(X2);
Xstandard=zscore(X3);
expressions=Xstandard;
%% subtipes selection

subtypes=X1(:,1);
subtypes= table2array(subtypes);
subtypes3=categorical(subtypes);


%% PCA calculation
[coeff, score, latent, tsquared, explained,mu] = pca(expressions); %if matrix do not contain blanks
X=score(:,1);
Y=score(:,2);
Z=score(:,3);

%% 3D Visualisation with "gscatter3b" function
gscatter3b(X,Y,Z, subtypes3, [0.82 0.3 0.1; 0.82 0.3 0.1; 0.82 0.3 0.1; 0 0.48 0.83; 0 0.48 0.83; 0 0.48 0.83;] ,'ox.ox.',[10,10,15,10,10,15]) ;