-
Notifications
You must be signed in to change notification settings - Fork 14
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
Implement variant association analysis (chi-square, linear/logistic regression) using Spark MLlib #126
Labels
Comments
jtarraga
added a commit
that referenced
this issue
Jun 1, 2017
jtarraga
added a commit
that referenced
this issue
Jun 1, 2017
jtarraga
changed the title
Implement variant analysis (linear/logistic regression, PCA...) using Spark MLlib
Implement variant association analysis (chi-square, linear/logistic regression) using Spark MLlib
Jun 6, 2017
jtarraga
added a commit
that referenced
this issue
Jun 7, 2017
jtarraga
added a commit
that referenced
this issue
Jun 7, 2017
jtarraga
added a commit
that referenced
this issue
Jun 7, 2017
jtarraga
added a commit
that referenced
this issue
Jun 7, 2017
jtarraga
added a commit
that referenced
this issue
Jun 7, 2017
jtarraga
added a commit
that referenced
this issue
Jun 9, 2017
jtarraga
added a commit
that referenced
this issue
Jun 9, 2017
jtarraga
added a commit
that referenced
this issue
Jun 13, 2017
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
The package should provide variant association analysis such as chi-square, linear and logistic regression. Spark's Machine Learning library (MLlib) provides a rich API to implement them in a bigdata environment.
Association tests:
Taking into account the following genetic models:
The text was updated successfully, but these errors were encountered: