mims-harvard / collage-dicty

Gene prioritization by compressive data fusion and chaining

Collage

Collage is a gene prioritization approach that relies on data fusion by collective matrix factorization. Its aim is to identify most promising candidate genes that may be associated with the phenotype of interest.

This repository contains supplementary code and data for Gene prioritization by compressive data fusion and chaining by Zitnik et al.

About Collage

In everyday life, we make decisions by considering all the available information, and often find that inclusion of even seemingly circumstantial evidence provides an advantage.

Collage can prioritize genes from a large collection of heterogeneous data. It considers data sets of various association levels with the prediction task, utilizes collective matrix factorization to compress the data, and chaining to relate different object types contained in a data compendium. Collage prioritizes genes based on their similarity to several seed genes.

We tested Collage by prioritizing bacterial response genes in Dictyostelium. Using 4 seed genes and 14 data sets, Collage proposed 8 candidate genes that were readily validated as necessary for the response of Dictyostelium to Gram-negative bacteria.

System requirements

Running this package will require a machine with 8 GB of RAM. Collective matrix factorization is a compute intensive procedure. Much of the analysis we mention in the paper was performed in parallel on a compute cluster.

Dependencies

The required dependencies to run the software are Numpy >= 1.8 and SciPy >= 0.10.

This code was last tested using Python 2.7.6.

Usage

collage.py - Demonstrates usage of Collage on data used to identify bacterial response genes in Dictyostelium.

See also scikit-fusion, our module for data fusion using collective latent factor models.

Citing

@article{Zitnik2015,
  title     = {Gene prioritization by compressive data fusion and chaining},
  author    = {{\v{Z}}itnik, Marinka and Nam, Edward A and Dinh, Christopher and
                Kuspa, Adam and Shaulsky, Gad and Zupan, Bla{\v{z}}},
  journal   = {PLoS Computational Biology},
  volume    = {11},
  number    = {10},
  pages     = {e1004552},
  year      = {2015},
  publisher = {PLOS}
}

License

Collage is licensed under the GPLv2.