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I scoured and searched the web for some implementation of digisome creation, but I had a hard time finding any good source code.

By digisomes, what I mean is images with chromosomal masks/borders that have been simulated. I am seeking this because I am trying to do a pixel level prediction on chromosomes using a deep network. And, I need ground truths with the chromosomes being illuminated. So ideally, I would like a binary image with just the chromosomes being illuminated.

How exactly are digisomes generated? Is a single actual chromosome extracted from some metaphase image, then morphed in interesting ways, and finally, all the different morphs are stitched together in one big collage format so that it there are numerous chromosomes all seemingly different?

Also, if someone could point me to some good source code, that'd be helpful.

Konrad Rudolph
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Jonathan
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  • Please [edit] your question and explain what digisomes are. I found one reference to them from the proceedings of a conference from 1990 and they seem to be referring to some form of in silico simulation of chromosomes. Is that what you mean? If the term is in use, it is not very common outside whatever specific subfield the authors may have been working in. Also, please explain what sort f analysis you would want to use these for since the kind of simulation/model you will need depends entirely on the questions you want to answer. – terdon Mar 16 '18 at 18:46
  • I've edited my question. Please let me know if that's not sufficient. – Jonathan Mar 16 '18 at 21:15
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    That's much better, thanks. I still don't get what you need, but now I'm sure that's just because of my own ignorance of the field. I also edited so that the extra information you added is integrated into the question. Since there's an edit history here, and the questions can (we hope) stick around for years, it's better to edit so that the question makes sense to someone reading it for the first time. – terdon Mar 16 '18 at 21:40

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I found a very nice github repo that simulates chromosomes and their labels for overlapping chromosomes - and with a little bit of tweaking, it can be amended for a variety of different simulations.

This was also used in a Kaggle post; so right up the alley for deep learning.

Jonathan
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