Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

exploring the data #11

Open
ypar opened this issue Jul 26, 2016 · 3 comments
Open

exploring the data #11

ypar opened this issue Jul 26, 2016 · 3 comments

Comments

@ypar
Copy link

ypar commented Jul 26, 2016

An issue has been raised in the meeting today regarding visualizations of the clinical data. Other data viz are also considered. However, more immediately, we need viz schemes of the clinical data for assessments and covariate selection.

@ypar
Copy link
Author

ypar commented Jul 26, 2016

@Inquisitive-Geek is interested in resolving this issue

@clairemcleod
Copy link
Member

Approach: seaborn/matplotlib in jupyter notebook

Potential visualizations:

Clinical:
  • Prevalence of tumor sites amongst samples
  • 'Time to event' distribution
  • Other variables of interest?
Sequencing (HiSeqV2):
  • Examine for batch effects? (potentially link to clinical matrix contributing variables)
Mutation:
  • Prevalence of mutation types
  • Number of mutations/sample ID
  • Most and least mutated genes

Feel free to add your own suggestions below!

@ypar
Copy link
Author

ypar commented Jul 28, 2016

my only worry with seaborn is that it is very memory heavy. However, for the scale of data, I suppose it will be ok.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants