Analysis of tissue-specific protein-protein interaction networks

The code in this repository implements an analysis pipeline for tissue-specific protein-protein interaction networks.

In this text we use the following abbreviations:

• PPI: protein-protein interaction network
• tsPPI: tissue-specific PPI

This is the Master thesis project of Patrick Flick. The Master thesis is available here. (See also docs). The thesis is licensed under the CC-BY 4.0 license.

Code organization

Different parts of the pipeline are accomplished by different programming languages. Python is used mainly for data import and processing. Most data comes in text format (tab separated or comma separated) from various sources. This data is imported and processed into a unified format and saved into a SQLite table.

Graph analysis is performed with the NetworKit parallel graph analysis library in order to speed up graph analysis tasks. To this end we implement our own extensions and extended algorithms for analyzing multiple Subgraphs simultaneously. This is useful, since all tsPPIs are represented as a set of subgraphs or a common PPI parent network. We implement this Subgraphs data structure and the extended algorithms using C++ and make them accessible inside our python based pipeline by extending NetworKit's cython API.

Final data analysis and visualization is done by a set of R scripts, making heavy use of the ggplot2 visualizations library.

The code is organized into different parts:

• src contains the python pipeline and other python scripts
• src/pappi contains all custom python modules used for the python pipeline
• analysis contains R scripts for analyzing data and generating figures and plots
• figs contains generated figures which are saved by the R scripts in analysis
• data contains the raw data and scripts to automatically download it
• ppi_networkit contains the Subgraph algorithms and the custom cython module, both of which extend the NetworKit graph library and it's cython python module.
• docs contains the master thesis document. This documents the algorithms and analysis implemented, and shows and interprets the results achieved.
• TODO: add submodule for fastSemSim

Installation

Dependencies

• python3 (sqlite3)
• g++ (version >= 4.7)
• scons (for NetworKit compilation)
• cython
• sqlite3-dev
• mercurial
• fastSemSim (only needed for running the BPScore benchmarks with bpscore_benchmark.py )

Compiling

To compile the NetworKit interacting cython module (including the extended algorithms for Subgraph datastructures), first install the before mentioned dependencies.

Then you can simply run the build.sh script in the main directory of the repository:

./build.sh

Data

Most of the datasets can can be automatically downloaded. Only a few need to be manually procured. See the data/README.md for details.

Running

Analysis Pipeline

Make sure all needed data is available in the data folder before proceeding.

Running the python pipeline consists of running 3 steps: (approximate run times are on a 4 core 3.4Ghz Intel CPU with 8 GiB RAM)

1. Data import and preprocessing (~10-20 minutes)
2. Running the graph analysis on all networks (~4-8 hours)
3. Running graph clustering/community-detection on all graphs (~6 hours)

To run this pipeline, execute the following:

cd src
python3 init_data.py
python3 graph_properties.py
python3 networkit_clustering.py

The final analysis and data visualization is implemented in R, all scripts are available in the analysis folder. Run these scripts to get the individual figures and graphs.

Algorithm benchmarks and tests

BPScore

The different BPScore algorithms can be benchmarked using the bpscore_benchmark.py script. To run this execute:

cd src
python3 bpscore_benchmark.py

Subgraph algoritms (ppi_networkit)

In order to execute the benchmarks and tests for the modified Subgraph algorithms implemented in the ppi_networkit cython module, run in the ppi_networkit folder:

For the tests:

./build/tests

And for the benchmarks:

./build/benchmark

Copyright Notice / Licensing

The code (src, ppi_networkit, analysis) is licensed under the MIT license (see LICENSE).

The Master Thesis and all figures (folder docs and figs) are licensed under the CC BY 4.0 (Creative Commons Attribution 4.0 International) license (see docs/LICENSE).