What zscore function does is simply ensure your data has zero mean and std = 1.
Say you have a matrix X where rows are measures and cols are different features (which are measured in different scales, so in order to compare one another you need to normalize them). Then you could obtained normalized X by simply doing:
X = [rand(100,1) 100*rand(100,1)];
% Take a look at how different the scales of X are
figure, plot(X,'o'), title('Unnormalized features');
% Now we can subtract the mean, per column:
X = bsxfun(@minus,X,mean(X,1));
% And make standard deviation equal to 1, per column:
X = bsxfun(@rdivide,X,std(X,[],1));
% Now they can be compared!
figure, plot(X,'o'), title('Normalized Features');
Now, if you have NaNs in your data, the only difference is that instead of using 'mean' or 'std' function you should use nanmean and nanstd, which will ignore the nans in your data:
% First let's put some NaNs
X = [rand(100,1) 100*rand(100,1)];
X(round(rand(20,1)*(numel(X)-1))+1) = NaN;
% And now normalize:
X = bsxfun(@minus,X,nanmean(X,1));
X = bsxfun(@rdivide,X,nanstd(X,[],1));
figure, plot(X,'o'), title('Normalized Features');
Remember that X, even after using nanmean and nanstd STILL HAS NaNs!!! If you wanna delete them, from each column:
X_without_nans = arrayfun(@(col) X(~isnan(X(:,col)),col),(1:size(X,2)),'un',0);
