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

In this text we use the following abbreviations:

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

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 done 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 oplots
• figs contains generated figures which are saved by the R scripts in analysis
• data contains the raw data and scripts to automatically download it
• TODO: add other repos (ppi_networkit, networkit and fastSemSim) and their dependencies

• python3 (sqlite3)
• NetworKit
• C++11 compiler
• fastSemSim

(see data folder readme)

TODO (building python modules

executing python pipeline executing benchmarks and tests of ppi_networkit plot all results