diffupath

Compute diffusion scores over networks from biological databases

These details have not been verified by PyPI

Project links

Project description

https://github.com/multipaths/diffupath/blob/master/docs/source/meta/diffupath_logo.png

Introduction

DiffuPath is an analytic tool for biological networks that connects the generic label propagation algorithms from DiffuPy to biological networks encoded in several formats such as Simple Interaction Format (SIF) or Biological Expression Language (BEL). For example, in the application scenario presented in the paper, we use three pathway databases (i.e., KEGG, Reactome and WikiPathways) and their integrated network retrieved from PathMe [1] to analyze three multi-omics datasets. However, other biological networks can be imported from the Bio2BEL ecosystem [2].

Installation

The latest stable code can be installed from PyPI with:

$ python3 -m pip install diffupath

The most recent code can be installed from the source on GitHub with:

$ python3 -m pip install git+https://github.com/multipaths/diffupath.git

For developers, the repository can be cloned from GitHub and installed in editable mode with:

$ git clone https://github.com/multipaths/diffupath.git
$ cd diffupath
$ python3 -m pip install -e .

Requirements

diffupath requires the following libraries:

networkx (>=2.1)
pybel (0.13.2)
biokeen (0.0.14)
click (7.0)
tqdm (4.31.1)
numpy (1.16.3)
scipy (1.2.1)
scikit-learn (0.21.3)
pandas (0.24.2)
openpyxl (3.0.2)
plotly (4.5.3)
matplotlib (3.1.2)
matplotlib_venn (0.11.5)
bio2bel (0.2.1)
pathme
diffupy

Command Line Interface

The following commands can be used directly from your terminal:

Download a database for network analysis.

The following command generates a BEL file representing the network of the given database.

$ python3 -m diffupath database get-database --database=<database-name>

To check the available databases, run the following command:

$ python3 -m diffupath database ls

Run a diffusion analysis

The following command will run a diffusion method on a given network with the given data

$ python3 -m diffupath diffusion diffuse --network=<path-to-network-file> --data=<path-to-data-file> --method=<method>

Run a diffusion analysis

$ python3 -m diffupath diffusion evaluate -i=<input_data> -n=<path_network>

Input Data

You can submit your dataset in any of the following formats:

CSV (.csv)
TSV (.tsv)

Please ensure that the dataset minimally has a column ‘Node’ containing node IDs. You can also optionally add the following columns to your dataset:

NodeType
LogFC [*]
p-value

Input dataset examples

DiffuPath accepts several input formats which can be codified in different ways. See the diffusion scores summary for more details.

You can provide a dataset with a column ‘Node’ containing node IDs.

Node
A
B
C
D

2. You can also provide a dataset with a column ‘Node’ containing node IDs as well as a column ‘NodeType’, indicating the entity type of the node to run diffusion by entity type.

Node	NodeType
A	Gene
B	Gene
C	Metabolite
D	Gene

3. You can also choose to provide a dataset with a column ‘Node’ containing node IDs as well as a column ‘logFC’ with their LogFC. You may also add a ‘NodeType’ column to run diffusion by entity type.

Node	LogFC
A	4
B	-1
C	1.5
D	3

4. Finally, you can provide a dataset with a column ‘Node’ containing node IDs, a column ‘logFC’ with their logFC and a column ‘p-value’ with adjusted p-values. You may also add a ‘NodeType’ column to run diffusion by entity type.

Node	LogFC	p-value
A	4	0.03
B	-1	0.05
C	1.5	0.001
D	3	0.07

You can also take a look at our sample datasets folder for some examples files.

Networks

In this section, we describe the types of networks you can select to run diffusion methods over. These include the following and are described in detail in this section [†]:

Select a network representing an individual biological database
Select multiple databases to generate a harmonized network
Select from one of four predefined collections of biological databases representing a harmonized network
Submit your own network [‡] from one of the accepted formats

Network Dumps

Because of the high computational cost of generating the kernel, we provide links to pre-calculated kernels for a set of networks representing biological databases.

Database	Description	Reference	Download
DDR	Disease-disease associations	[3]	ddr.json
DrugBank	Drug and drug target interactions	[4]	drugbank.json
Gene Ontology	Hierarchy of tens of thousands of biological processes	[5]	go.json
HSDN	Associations between diseases and symptoms	[6]	hsdn.json
KEGG	Multi-omics interactions in biological pathways	[7]	kegg.json
miRTarBase	Interactions between miRNA and their targets	[8]	mirtarbase.json
Reactome	Multi-omics interactions in biological pathways	[9]	reactome.json
SIDER	Associations between drugs and side effects	[10]	sider.json
WikiPathways	Multi-omics interactions in biological pathways	[11]	wikipathways.json

If you would like to use one of our predefined collections, you can similarly download pre-calculated kernels for sets of networks representing integrated biological databases.

Collection	Database	Description	Download
#1	KEGG, Reactome and WikiPathways	-omics and biological processes/pathways	pathme.json
#2	KEGG, Reactome, WikiPathways and DrugBank	-omics and biological processes/pathways with a strong focus on drug/chemical interactions	pathme_drugbank.json
#3	KEGG, Reactome, WikiPathways and MirTarBase	-omics and biological processes/ pathways enriched with miRNAs	pathme_mirtarbase.json

Custom-network formats

You can also submit your own networks in any of the following formats:

BEL (.bel)
CSV (.csv)
Edge list (.lst)
GML (.gml or .xml)
GraphML (.graphml or .xml)
Pickle (.pickle)
TSV (.tsv)
TXT (.txt)

Minimally, please ensure each of the following columns are included in the network file you submit:

Source
Target

Optionally, you can choose to add a third column, “Relation” in your network (as in the example below). If the relation between the Source and Target nodes is omitted, and/or if the directionality is ambiguous, either node can be assigned as the Source or Target.

Custom-network example

Source	Target	Relation
A	B	Increase
B	C	Association
A	D	Association

You can also take a look at our sample networks folder for some examples.

Disclaimer

DiffuPath is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.

References

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

This version

0.0.3

May 13, 2020

0.0.2

Apr 12, 2020

0.0.1

Mar 13, 2020

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

diffupath-0.0.3.tar.gz (47.4 kB view details)

Uploaded May 13, 2020 Source

File details

Details for the file diffupath-0.0.3.tar.gz.

File metadata

Download URL: diffupath-0.0.3.tar.gz
Upload date: May 13, 2020
Size: 47.4 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/1.14.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.4

File hashes

Hashes for diffupath-0.0.3.tar.gz
Algorithm	Hash digest
SHA256	`0dd5d5b7d05ec54d25748ed948706b020bd4d974e5cb13bfcae1c4466e2a9fca`
MD5	`8be24411088330bfbdb4ac5b849a7913`
BLAKE2b-256	`0a4fe6bbf87a8c5dbf9e1b2cfc554d96c61a9c0645ba4afd9e9543f32238cb7c`