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How I can make the natural history plot clearer

Open MaryGoAround opened this issue 5 years ago • 2 comments

Hello

In the natural history of tumours, chromosomal arms have been intermingled and not clear like the attached plot

By which argument we can make that readable please?

palimpsest_plotTumorHistories function does not have such an argument

LP6008334_DNA_A03.pdf

MaryGoAround avatar Oct 26 '20 21:10 MaryGoAround

Hi,

There are probably ways to avoid overlapping labels in R but we have not had the time to implement this in Palimpsest yet. I would suggest manually correcting the plot in Adobe Illustrator or Powerpoint, by removing existing labels and writing the list of simultaneous gains below, separated by commas, as we did in Fig. 6 of this paper: https://www.nature.com/articles/s41467-017-01358-x https://www.nature.com/articles/s41467-017-01358-x If all the genome is duplicated you may also simply write « whole genome duplication ».

I hope it helps! Best, Eric

Le 26 oct. 2020 à 22:53, MaryGoAround [email protected] a écrit :

Hello

In the natural history of tumours, chromosomal arms have been intermingled and not clear like the attached plot

By which argument we can make that readable please?

palimpsest_plotTumorHistories function does not have such an argument

LP6008334_DNA_A03.pdf https://github.com/FunGeST/Palimpsest/files/5441744/LP6008334_DNA_A03.pdf — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/FunGeST/Palimpsest/issues/53, or unsubscribe https://github.com/notifications/unsubscribe-auth/AHIZAX2R6NLLJV5P37NFZJ3SMXVXLANCNFSM4S765FTA.

FunGeST avatar Oct 27 '20 09:10 FunGeST

Thank you so much

I have converted the pdf from natural history to powerpoint

like this Duplication_timing.pdf Screenshot 2020-10-30 at 17 58 10

If for instance, I am interested in peak 8q, from which part of the output of your software I can say that the gain of 8q has happened after how much percent of mutations? I mean is there any part of the output to achieve this?

I see a dataframe name point.mut.time, is this information here? If so which part of that say the gain has happened after how much percent of the mutations?

beginner984 avatar Oct 30 '20 18:10 beginner984