Tied Diffusion for Subnetwork Discovery (TieDIE)
Python R Matlab Shell
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examples
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pathways
test
FAQ.txt
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README.md

README.md

TieDIE: Tied Diffusion for Subnetwork Discovery.

Current Version

1.0

Authors

Evan O. Paull, Daniel Carlin and Joshua M. Stuart.

Additional Contributors

Srikanth Bezawada (TieDIE Cytoscape Plugin) Josh L. Espinoza (Quick kernel loading feature) Dana Silverbush (MATLAB kernel generation code updates to newer versions)

Requirements

Python 2.7 and the python numpy module are required to run the tiedie executable, when using a pre-computed diffusion kernel.

Either MATLAB or the python scipy module, version 0.12 or later, is required for diffusion kernel computation: the latter is free, though not as computationally efficient as the MATLAB implementation.

Installation

  • Install dependencies
  • Download the TieDIE repository to the desired location
  • (Recommended) Pre-Generate kernel file with MATLAB (bin/makeKernel.sh)
  • Run TieDIE/bin/tiedie

Examples

  • GBM.test An example signaling network from Glioblastoma (TCGA Network, 2012) is provided, along with input heats for an upstream set of genes (mutated genes) and a downstream set of nodes (transcriptional responses). To run:

    cd examples/GBM.test make

A tutorial for this simple example is provided in doc/Tutorial.pdf.

Programs

  • tiedie Python executable to run the TieDIE algorithm.
  • makeKernel.sh Shell script executable that calls MATLAB for diffusion kernel file generation.
  • (Auxillary) span.R An R-implementation of the Prize Collecting Steiner Tree network formulation, that calls the BioNet package.

Folders

  • bin : executables and matlab source files
  • lib : python code libraries for the tiedie executable
  • test : doctest unit tests, functional tests and regression tests
  • examples : GBM and BRCA inputs for demonstration purposes
  • galaxy : Galaxy web-server wrapper for tiedie to run through the web interface. (https://main.g2.bx.psu.edu/)
  • pathways : the "superpathway" described in the TieDIE paper, used with the TCGA BRCA dataset

In Press

TieDIE was first featured in the 2013 Nature paper "Comprehensive molecular characterization of clear cell renal cell carcinoma". In this TCGA (The Cancer Genome Atlas) network publication, a TieDIE analysis was used to connnect frequently mutated genes involving the SWI/SNF chromatin remodelling complex to a diverse set of gene expression changes characteristic of tumor development and progression. The TieDIE manuscript was not yet published at the time of the Nature publication and so is cited by name and author only. The TieDIE network solution is shown in figure 4 of the main text, which can be found at this link: http://www.nature.com/nature/journal/v499/n7456/full/nature12222.html .

Contact

Feature requests, comments and requests for clarification should all be sent to the author at epaull@soe.ucsc.edu. I will try to respond quickly to all requests, so feel free to email me!