The art of blowing up protein

Not much else can be done with the data because many of the single-appearance words are actually sequences and there is no way to cluster the words by meaning. All the possible analyses will give generic results (e.g. more frequent words are shorter). If one were to go overboard, one option would be to track the progress of certain key words across the text. A Twitter trending equivalent. The problem is that I already know what words will appear more frequently were. So there isn't much point and it is probably best stopping at a Wordle step, which shows what one already knows, but is pretty.

Appendix, Scripts:

%first graph
loglog(x,'.');
ylabel('Counts');
xlabel('Rank of the unique word');
title('Word frequency in Matteo"s thesis');
offset= 300;
text(7,x(7)+offset,'MetC');
hold on;
plot(7,x(7),'r.','Markersize',20)
text(17,x(17)+offset,'enzyme')
plot(17,x(17),'r.','Markersize',20)
hold off;

>%second graph (not shown, frequent words are shorter).
x(isnan(x))=1;
xprime=unique(x);
z=cellfun(@length,words);
zprime=[];
yprime=[];
for i=1:numel(xprime)
    zprime=[zprime mean(z(x==xprime(i)))];
    yprime=[yprime numel(z(x==xprime(i)))];
end
plot(xprime,zprime,'ob');
hold on;
plot(xprime(yprime>1),zprime(yprime>1),'or');
hold off;
xlabel('number of counts of word');

ylabel('average number of letters for words of that frequency')