PyPathway

integrated Python toolkit for pathway based analysis

Installation

Install PyPathway via Anaconda is recommended.

1. Download and install anaconda from Anaconda site
2. Install PyPathway by

conda install -c steamedsheep pypathway

NOTE: If you want to install pypathway via pypi, please refer to the Installation section

Documentation and tests

View the docs at github.io

Regular tests and notebook (nbval) tests

Features

• Public databases APIs: STRING, BioGRID, KEGG, Reactome and WikiPathway
• Functional set based and network based enrichment analysis algorithms implemented: ORA, GSEA and SPIA
• Performance optimize for denovo enrichment algorithm MAGI and Hotnet2.
• Network propagation algorithms random walk, RWR and heat kernel.
• Interactive visualization and web page exportation for pathway, graph and analysis result.
• Integrated with pandas, networkx and numpy. Most of the methods accept both text file and data structure from these packages
• Dynamic visualization for IPython notebook.
• Most classes implement __repr__ method for interactive environment.

Network process

Intuitive APIs for querying and retrieval interaction network from public database. The return object are stored in networkx.Graph object.

Support databases

• KEGG
• Reactome
• WikiPathway
• STRING
• BioGRID

Search

from pypathway import PublicDatabase
kg = PublicDatabase.search_kegg('CD4')
wp = PublicDatabase.search_wp('CD4')
rt = PublicDatabase.search_reactome('CD4')

Load

pathway = r[0].load()

Plot

pathway.draw()

IPython notebook examples

• STRING and BioGRID
• Pathway databases

Enrichment Analysis

Support methods

• ORA
• GSEA
• Network enrichment (SPIA and Enrichment)
• denovo enrichment (MAGI and Hotnet2)

Implementation / Interface

• Staticmethod run() for the starting of the analysis

r = SPIA.run(all=c.background, de=c.deg, organism='hsa')

• table, plot() and graph() method for the presentation of the analysis

res.table

res.plot()

res.graph()

IPython examples

• ORA
• GSEA
• Network enrichment
• MAGI

Modeling

• the Python Interface and optimize for MAGI
• several c extension for Hotnet permutation performance

Propagation

Implemented algorithms

• Random walk

random_walk(G, h)

• Random walk with restart

random_walk_with_restart(G, h, rp=0.7, n=-1)

• Heat kernel

diffusion_kernel(G, h, rp=0.8, n=100)

Implementation detail

image source: Network propagation: a universal amplifier of genetic associations

IPython notebook examples

• Propagation

Utility and Performance

• The Id converter
• GMT file manager
• network and expression data sets.
• numpy implementation of SPIA
• node swap c extension for Hotnet2
• multi-threading for MAGI