tuskaw / scikit-fusion

scikit-fusion: Data fusion via collective latent factor models

scikit-fusion

scikit-fusion is a Python module for data fusion and learning over heterogeneous datasets. The core of scikit-fusion are recent collective latent factor models and large-scale joint matrix factorization algorithms.

[News:] Fast CPU and GPU-accelerated implementatons of some of our methods.

[News:] Scikit-fusion, collective latent factor models, matrix factorization for data fusion and learning over hetnets.

[News:] fastGNMF, fast implementation of graph-regularized non-negative matrix factorization using Facebook FAISS.

Dependencies

scikit-fusion is tested to work under Python 3.

The required dependencies to build the software are Numpy >= 1.7, SciPy >= 0.12, PyGraphviz >= 1.3 (needed only for drawing data fusion graphs) and Joblib >= 0.8.4.

Install

This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:

python setup.py install --user

To install for all users on Unix/Linux:

python setup.py build
sudo python setup.py install

For development mode use:

python setup.py develop

Use

Let's generate three random data matrices describing three different object types:

 >>> import numpy as np
 >>> R12 = np.random.rand(50, 100)
 >>> R13 = np.random.rand(50, 40)
 >>> R23 = np.random.rand(100, 40)

Next, we define our data fusion graph:

 >>> from skfusion import fusion
 >>> t1 = fusion.ObjectType('Type 1', 10)
 >>> t2 = fusion.ObjectType('Type 2', 20)
 >>> t3 = fusion.ObjectType('Type 3', 30)
 >>> relations = [fusion.Relation(R12, t1, t2),
                  fusion.Relation(R13, t1, t3),
                  fusion.Relation(R23, t2, t3)]
 >>> fusion_graph = fusion.FusionGraph()
 >>> fusion_graph.add_relations_from(relations)

and then collectively infer the latent data model:

 >>> fuser = fusion.Dfmf()
 >>> fuser.fuse(fusion_graph)
 >>> print(fuser.factor(t1).shape)
 (50, 10)

Afterwards new data might arrive:

 >>> new_R12 = np.random.rand(10, 100)
 >>> new_R13 = np.random.rand(10, 40)

for which we define the fusion graph:

 >>> new_relations = [fusion.Relation(new_R12, t1, t2),
                      fusion.Relation(new_R13, t1, t3)]
 >>> new_graph = fusion.FusionGraph(new_relations)

and transform new objects to the latent space induced by the fuser:

 >>> transformer = fusion.DfmfTransform()
 >>> transformer.transform(t1, new_graph, fuser)
 >>> print(transformer.factor(t1).shape)
 (10, 10)

---

scikit-fusion contains several applications of data fusion:

>>> from skfusion import datasets
>>> dicty = datasets.load_dicty()
>>> print(dicty)
FusionGraph(Object types: 3, Relations: 3)
>>> print(dicty.object_types)
{ObjectType(GO term), ObjectType(Experimental condition), ObjectType(Gene)}
>>> print(dicty.relations)
{Relation(ObjectType(Gene), ObjectType(GO term)),
 Relation(ObjectType(Gene), ObjectType(Gene)),
 Relation(ObjectType(Gene), ObjectType(Experimental condition))}

Selected publications (Methods)

• Data fusion by matrix factorization: http://dx.doi.org/10.1109/TPAMI.2014.2343973
• Jumping across biomedical contexts using compressive data fusion: https://academic.oup.com/bioinformatics/article/32/12/i90/2240593
• Survival regression by data fusion: http://www.tandfonline.com/doi/abs/10.1080/21628130.2015.1016702
• Gene network inference by fusing data from diverse distributions: https://academic.oup.com/bioinformatics/article/31/12/i230/216398
• Fast optimization of non-negative matrix tri-factorization: https://doi.org/10.1371/journal.pone.0217994

Selected publications (Applications)

• A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family: https://www.nature.com/articles/s41467-017-01642-w
• Gene prioritization by compressive data fusion and chaining: http://dx.doi.org/10.1371/journal.pcbi.1004552
• Discovering disease-disease associations by fusing systems-level molecular data: http://www.nature.com/srep/2013/131115/srep03202/full/srep03202.html
• Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold: http://www.worldscientific.com/doi/pdf/10.1142/9789814583220_0038
• Matrix factorization-based data fusion for drug-induced liver injury prediction: http://www.tandfonline.com/doi/abs/10.4161/sysb.29072
• Collective pairwise classification for multi-way analysis of disease and drug data: https://doi.org/10.1142/9789814749411_0008

Tutorials

• Large-scale data fusion by collective matrix factorization, Basel Computational Biology Conference, [BC]^2 [Slides] [Handouts]
• Data fusion of everything, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC [Slides] [Handouts]