Here's one way to do it:
% read the file:
data = readcell('example_data.csv');
data(1:10,:)
% get the types and ids:
C = regexp(data(1,:),'(.+)_(.+)','tokens');
C = [C{:}];
type_id = vertcat(C{:})
% do the reshaping and column-prepending operations:
[nrows,ncols] = size(data);
new_data = ['SampleType' 'replicateID' data(2,1:4); ... % first row: variable names
repelem(type_id,(nrows-2)/4,1) ... % first two columns: types and IDs
reshape(permute(reshape(data(3:end,:),[],4,ncols/4),[1 3 2]),[],4)]; % columns 3-6: numeric data cells
% fill "missing" cells with a scalar NaN value:
new_data(cellfun(@(x)isa(x,'missing'),new_data)) = {NaN};
new_data(1:10,:) % check the top
new_data(nrows+(-5:4),:) % check the transition from nonyeasted to yeasted
% write the new file:
writecell(new_data,'example_data_modified.csv');
Read the documentation for reshape, permute, and repelem to understand how they are used here.
