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Richarizardd avatar Richarizardd commented on May 24, 2024

Hi @omniaalwazzan - grade (or any clinical metadata) is not used in the downstream tasks.

The clinical metadata is separated from the genomic features.

def getCleanGBMLGG(dataroot='./data/TCGA_GBMLGG/', ignore_missing_moltype=False, ignore_missing_histype=False, use_rnaseq=False, use_ag=False):

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omniaalwazzan avatar omniaalwazzan commented on May 24, 2024

Thanks for answering @Richarizardd

Yes that makes sense!

I was quite confused as I used the exact function you referred (line 49) which joins the grade with genomics data; so I thought you were using them :)

Moreover, the Fig.3(A) shown the grade with the molecular subtype puzzled me a bit as if molecular subtype was accounted as one of the features!

Thanks,
Omnia.

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Richarizardd avatar Richarizardd commented on May 24, 2024

Ah apologies for the confusion @omniaalwazzan. In Figure 3A, it is showing how the risks are distributed across the different histomolecular subtypes, from which you see that IDHwt-astrocytomas are prognosticated with higher risk scores.

Moreover, we see that the grade also correlates with the subtypes, e.g. - most of the IDHwt-astrocytomas that are predicted high risk are also Grade IV.

All in all, this plot is to help corroborate that the predicted risk scores corroborate with known biomarkers and current grading paradigms.

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omniaalwazzan avatar omniaalwazzan commented on May 24, 2024

Thanks a lot for this clarification!

My confusion is resolved.

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