Compute diffusion scores over networks from biological databases
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  to analyze three multi-omics datasets. However, other biological networks can be imported from the Bio2BEL ecosystem .
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 .
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>
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:
- LogFC [*]
|[*]||Log2 fold change|
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.
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.
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.
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.
You can also take a look at our sample datasets folder for some examples files.
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
|[†]||Please note that all networks available through DiffuPath have been generated using PyBEL v.0.13.2.|
|[‡]||If there are duplicated nodes in your network, please take a look at this Jupyter Notebook to address the issue.|
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.
|DrugBank||Drug and drug target interactions||||drugbank.json|
|Gene Ontology||Hierarchy of tens of thousands of biological processes||||go.json|
|HSDN||Associations between diseases and symptoms||||hsdn.json|
|KEGG||Multi-omics interactions in biological pathways||||kegg.json|
|miRTarBase||Interactions between miRNA and their targets||||mirtarbase.json|
|Reactome||Multi-omics interactions in biological pathways||||reactome.json|
|SIDER||Associations between drugs and side effects||||sider.json|
|WikiPathways||Multi-omics interactions in biological pathways||||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.
|#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|
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:
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.
You can also take a look at our sample networks folder for some examples.
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.
|||Domingo-Fernandez, D., Mubeen, S., Marin-Llao, J., Hoyt, C., et al. Hofmann-Apitius, M. (2019). PathMe: Merging and exploring mechanistic pathway knowledge.. BMC Bioinformatics, 20:243.|
|||Hoyt, C. T., et al. (2019). Integration of Structured Biological Data Sources using Biological Expression Language. bioRxiv, 631812.|
|||Menche, J., et al. (2015). Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science, 347(6224), 1257601.|
|||Wishart, D. S., et al. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.|
|||Ashburner, M., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics, 25(1), 25–9.|
|||Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature communications, 5(1), 1-10.|
|||Kanehisa, et al. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs.. Nucleic Acids Res. 45,D353-D361.|
|||Huang, H. Y., et al. (2020). miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic acids research, 48(D1), D148-D154.|
|||Fabregat, A et al. (2016). The Reactome Pathway Knowledgebase. Nucleic Acids Research 44. Database issue: D481–D487.|
|||Kuhn, M., et al. (2016). The SIDER database of drugs and side effects. Nucleic Acids Research, 44(D1), D1075–D1079.|
|||Slenter, D.N., et al. (2017). WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, 46(D1):D661-D667.|
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