pywikipathways and py4cytoscape

by Kozo Nishida, Barry Demchak, Alexander Pico

pywikipathways 0.0.2

py4cytoscape 0.0.10

WikiPathways is a well-known repository for biological pathways that provides unique tools to the research community for content creation, editing and utilization [@Pico2008].

Python is a powerful programming language and environment for statistical and exploratory data analysis.

pywikipathways leverages the WikiPathways API to communicate between Python and WikiPathways, allowing any pathway to be queried, interrogated and downloaded in both data and image formats. Queries are typically performed based on “Xrefs”, standardized identifiers for genes, proteins and metabolites. Once you can identified a pathway, you can use the WPID (WikiPathways identifier) to make additional queries.

py4cytoscape leverages the CyREST API to provide a number of functions related to network visualization and analysis.

Prerequisites

In addition to this pywikipathways package, you’ll also need to install cytoscape and py4cytoscape:

!pip install pywikipathways

The whole point of py4cytoscape is to connect with Cytoscape. You will need to install and launch Cytoscape:

• Download the latest Cytoscape from http://www.cytoscape.org/download.php

• Complete installation wizard

• Launch Cytoscape

• Install FileTransfer App (Follow here to do it.)

NOTE: To run this notebook, you must manually start Cytoscape first – don’t proceed until you have started Cytoscape.

Setup required only in a remote notebook environment

If you’re using a remote Jupyter Notebook environment such as Google Colab, run the cell below. (If you’re running a local Jupyter Notebook server on the desktop machine same with Cytoscape, you don’t need to do that.)

_PY4CYTOSCAPE = 'git+https://github.com/cytoscape/py4cytoscape@0.0.10'
import requests
exec(requests.get("https://raw.githubusercontent.com/cytoscape/jupyter-bridge/master/client/p4c_init.py").text)
IPython.display.Javascript(_PY4CYTOSCAPE_BROWSER_CLIENT_JS) # Start browser client

Getting started

First, confirm that you have everything installed and running:

import pywikipathways as pwpw

import py4cytoscape as p4c

p4c.cytoscape_ping()

You are connected to Cytoscape!

'You are connected to Cytoscape!'

For this vignette, you’ll also need the WikiPathways app to access the WikiPathways database from within Cytoscape. Install the WikiPathways app from http://apps.cytoscape.org/apps/wikipathways

If you are running Cytoscape 3.7.0 or above, you can simply run this command:

p4c.install_app('WikiPathways') #only available in Cytoscape 3.7.0 and above

{}

{}

Working together

Ok, with all of these components loaded and launched, you can now perform some nifty sequences. For example, search for a pathway based on a keyword search and then load it into Cytoscape.

gbm_pathways = pwpw.find_pathways_by_text('Glioblastoma') # many pathways returned

gbm_pathways

	score	id	url	name	species	revision
0	1.0189675	WP3593	https://www.wikipathways.org/index.php/Pathway...	MicroRNA for targeting cancer growth and vascu...	Homo sapiens	117786
1	0.79327244	WP2261	https://www.wikipathways.org/index.php/Pathway...	Glioblastoma signaling pathways	Homo sapiens	118379
2	0.79327244	WP3266	https://www.wikipathways.org/index.php/Pathway...	Glioblastoma signaling pathways	Bos taurus	116688
3	0.2698266	WP4398	https://www.wikipathways.org/index.php/Pathway...	PDGFR-alpha and STMN1 cooperate to exacerbate ...	Mus musculus	117929
4	0.047133956	WP3998	https://www.wikipathways.org/index.php/Pathway...	Prader-Willi and Angelman syndrome	Homo sapiens	119303

human_gbm_pathways = gbm_pathways[gbm_pathways['species'] == "Homo sapiens"] # just the human gbm  pathways

human_gbm_pathways

	score	id	url	name	species	revision
0	1.0189675	WP3593	https://www.wikipathways.org/index.php/Pathway...	MicroRNA for targeting cancer growth and vascu...	Homo sapiens	117786
1	0.79327244	WP2261	https://www.wikipathways.org/index.php/Pathway...	Glioblastoma signaling pathways	Homo sapiens	118379
4	0.047133956	WP3998	https://www.wikipathways.org/index.php/Pathway...	Prader-Willi and Angelman syndrome	Homo sapiens	119303

We have a list of human pathways that mention Glioblastoma The results include lots of information, so let’s get a unique list of just the WPIDs.

human_gbm_wpids = human_gbm_pathways['id']

human_gbm_wpids

0    WP3593
1    WP2261
4    WP3998
Name: id, dtype: object

Let’s import the first one of these into Cytoscape!

p4c.commands_run('wikipathways import-as-pathway id=' + human_gbm_wpids[0])

[]

p4c.notebook_show_image()

Once in Cytoscape, you can load data, apply visual style mappings, perform analyses, and export images and data formats. See py4cytoscape package document for details.

From networks to pathways

If you are already with with networks and data in Cytoscape, you may end up focusing on one or few particular genes, proteins or metabolites, and want to query WikiPathways.

For example, let’s open a sample network from Cytoscape and identify the gene with the largest number of connections, i.e., node degree.

Note: this next chunk will overwrite your current session. Save if you want to keep anything.

p4c.open_session()

Opening sampleData/sessions/Yeast Perturbation.cys...

{}

p4c.notebook_show_image()

net_data = p4c.get_table_columns(columns=['name','degree.layout','COMMON'])

net_data

	name	degree.layout	COMMON
159	YDL194W	1	SNF3
160	YDR277C	2	MTH1
161	YBR043C	1	YBR043C
162	YPR145W	1	ASN1
163	YER054C	2	GIP2
...	...	...	...
484	YOR204W	1	DED1
485	YGL097W	3	SRM1
486	YGR218W	1	CRM1
487	YGL122C	3	NAB2
488	YKR026C	1	GCN3

330 rows × 3 columns

net_data['degree.layout'].max()

18

max_gene = net_data[net_data['degree.layout']==18]

max_gene

	name	degree.layout	COMMON
365	YMR043W	18	MCM1

Great. It looks like MCM1 has the larget number of connections (18) in this network. Let’s use it’s identifier (YMR043W) to query WikiPathways to learn more about the gene and its biological role, and load it into Cytoscape.

Pro-tip: We need to know the datasource that provides a given identifier. In this case, it’s sort of tricky: Ensembl provides these Yeast ORF identifiers for this organism rather than they typical format. So, we’ll include the ‘En’ system code. See other vignettes for more details.

mcm1_pathways = pwpw.find_pathway_ids_by_xref('YMR043W','En').unique()

p4c.commands_run('wikipathways import-as-pathway id=' + mcm1_pathways[0])

[]

p4c.notebook_show_image()

And we can easily select the MCM1 node by name in the newly imported pathway to help see where exactly it plays its role.

p4c.select_nodes('MCM1','name')

{'nodes': [1615, 1618], 'edges': []}

p4c.notebook_show_image()