Package: jive
This version of the package is now deprecated! The new version of the AJIVE code can be found underhttps://github.com/idc9/mvdrjiveauthor: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.Overviewjiveis a data analysis package for high-dimensional, multi-block
(or multi-view) data. The multi-block data setting means two or more data
matrices with a fixed set of observations (e.g. patients) and multiple
sets of features (e.g. clinical features and gene expression data).The primary algorithm in this package is Angle based Joint and
Individual Variation Explained (AJIVE) which is a data integration/feature
extraction algorithm. AJIVE finds joint modes of variation which are
common to all K data blocks as well as modes ofindividualvariation which
are specific to each block. For a detailed discussion of AJIVE seeAngle-Based Joint and Individual Variation Explained. An R version of
AJIVE can be foundhere.InstallationTo install use pip:::pip install jiveOr clone the repo:::git clone https://github.com/idc9/py_jive.git
python setup.py installjive is currently available for python 3Example.. code:: pythonfrom jive.AJIVE import AJIVE
from jive.PCA import PCA
from jive.ajive_fig2 import generate_data_ajive_fig2
from jive.viz.block_visualization import data_block_heatmaps, jive_full_estimate_heatmaps
import matplotlib.pyplot as plt
# %matplotlib inline

X, Y = generate_data_ajive_fig2()
data_block_heatmaps([X, Y]).. image:: doc/figures/data_heatmaps.png.. code:: python# determine initial signal ranks by inspecting scree plots
plt.figure(figsize=[10, 5])
plt.subplot(1, 2, 1)
PCA().fit(X).plot_scree()
plt.subplot(1, 2, 2)
PCA().fit(Y).plot_scree().. image:: doc/figures/scree_plots.png.. code:: pythonajive = AJIVE(init_signal_ranks={'x': 2, 'y': 3})
ajive.fit(blocks={'x': X, 'y': Y})

plt.figure(figsize=[10, 20])
jive_full_estimate_heatmaps(ajive.get_full_block_estimates(),
                            blocks={'x': X, 'y': Y}).. image:: doc/figures/jive_estimate_heatmaps.png.. code:: pythonajive.plot_joint_diagnostic().. image:: doc/figures/jive_diagnostic.pngHelp and SupportAdditional documentation, examples and code revisions are coming soon.
For questions, issues or feature requests please reach out to Iain:iain@unc.edu.Documentation
^^^^^^^^^^^^^The source code is located on github:https://github.com/idc9/py\_jive. Currently the best math reference
is theAJIVE paper.Testing
^^^^^^^Testing is done usingnose_.Contributing
^^^^^^^^^^^^We welcome contributions to make this a stronger package: data examples,
bug fixes, spelling errors, new features, etc.Citation
^^^^^^^^.. image::https://zenodo.org/badge/94366513.svg:target:https://zenodo.org/badge/latestdoi/94366513.. _Iain Carmichael:https://idc9.github.io/.. _Angle-Based Joint and Individual Variation Explained:https://arxiv.org/pdf/1704.02060.pdf.. _here:https://github.com/idc9/r_jive.. _these example notebooks: doc/example_notebooks/
.. _https://github.com/idc9/py\_jive:https://github.com/idc9/r_jive.. _AJIVE paper:https://arxiv.org/pdf/1704.02060.pdf.. _nose:http://nose.readthedocs.io/en/latest/.. _Journal of Statistical Software:https://www.jstatsoft.org/index