author: Iain Carmichael
Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.
This package performs association tests between the observed data and their systematic patterns of variation. This package implements methods from (Chung and Storey, 2015) in python. For an R version (which we followed closely) see https://github.com/ncchung/jackstraw.
Chung, N.C. and Storey, J.D. (2015) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics, 31(4): 545-554 http://bioinformatics.oxfordjournals.org/content/31/4/545
This is currently an informal package under development so I've only made it installable from github.
git clone https://github.com/idc9/jackstraw.git python setup.py install
import numpy as np
from jackstraw.jackstraw import Jackstraw
X = np.random.normal(size=(100, 20))
jack = Jackstraw(S = 10, B = 100)
jack.fit(X, method='pca', rank=4)
jack.rejected
array([], dtype=int64)
For some more example code see these example notebooks.
Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.
The source code is located on github: https://github.com/idc9/jackstraw.
Testing is done using nose.
We welcome contributions to make this a stronger package: data examples, bug fixes, spelling errors, new features, etc.