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Instant-use add-on functions for the Wolfram Language

Function Repository Resource:

 HumanProteinInteractionNetwork

Source Notebook

Generate a graph of a specific portion of the human protein-protein interaction network for a gene

Contributed by: Annie Zhu

ResourceFunction["HumanProteinInteractionNetwork"][gene]

creates a Graph of a specific portion of the human protein-protein interaction network given a gene or node of the network.

Details and Options

The data is based on The Human Protein Atlas version 23.0 and Ensembl version 109.
Each vertex in the resulting graph represents a protein. The edges represent physical interactions between proteins (which often share the same name as the gene that codes for the protein).
These protein-protein interactions lie at the heart of all biological functions in our body.
ResourceFunction["HumanProteinInteractionNetwork"] accepts all options of Graph and GraphPlot, the ResourceFunction VertexSizeScaledGraph and the following Options:
"NetworkLayers"1how many layers from the starting gene is considered
"ScaledGraph"FalseVertexSize of the Graph scaled based on different graph measures
GraphPlot is used when total EdgeCount is more than 10000 and "ScaledGraph"-> True.
If "NetworkLayers" is 1, then we consider only the nodes connected to the input node or gene. If it is 2, then we consider all connections to the input and the connections to all these connections and so on.

Examples

Basic Examples (1) 

HumanProteinInteractionNetwork for gene A1CF:

In[1]:=
ResourceFunction["HumanProteinInteractionNetwork"]["A1CF"]
Out[1]=

Scope (1) 

Let us find the genes interacting with A1CF:

In[2]:=
ResourceFunction["HumanProteinInteractionNetwork"]["A1CF", VertexLabels -> "Name"]
Out[2]=
In[3]:=
DeleteCases[ VertexList@ ResourceFunction["HumanProteinInteractionNetwork"]["A1CF"], "A1CF"]
Out[3]=

Options (2) 

NetworkLayers (1) 

Choose the desired depth of the network:

In[4]:=
ResourceFunction["HumanProteinInteractionNetwork"]["AQP6", "NetworkLayers" -> 2]
Out[4]=

ScaledGraph (1) 

Choose if the VertexSize of the Graph is scaled based on its VertexDegree. When "ScaledGraph"->True we can pass other Options for ResourceFunction["VertexSizeScaledGraph"]:

In[5]:=
ResourceFunction["HumanProteinInteractionNetwork"]["A1CF", "NetworkLayers" -> 2, "ScaledGraph" -> True, "MaxVertexSize" -> 40, VertexStyle -> RGBColor[0.91, 0.68, 0], EdgeStyle -> RGBColor[0, 0.58, 0.61], VertexLabels -> Placed["Name", Tooltip]]
Out[5]=

Properties and Relations (2) 

For a really large graph, the Graph is shown as a summary box:

In[6]:=
gr = ResourceFunction["HumanProteinInteractionNetwork"]["C9orf24", "NetworkLayers" -> 5]
Out[6]=

GraphPlot can be used for visualization:

In[7]:=
GraphPlot@gr
Out[7]=

Neat Examples

In[8]:=
ResourceFunction["HumanProteinInteractionNetwork"]["C9orf24", "NetworkLayers" -> 2, "ScaledGraph" -> True, "MaxVertexSize" -> 40, VertexStyle -> RGBColor[0.91, 0.68, 0], EdgeStyle -> RGBColor[0, 0.58, 0.61], GraphLayout -> "BalloonEmbedding", VertexLabels -> Placed["Name", Tooltip]]
Out[8]=

Publisher

WolframChemistry

Version History

  • 1.0.0 – 10 January 2025

Source Metadata

  • Citation:
    • Uhlén M et al., Tissue-based map of the human proteome. Science (2015) PubMed: 25613900 DOI: 10.1126/science.1260419.
    • Thul PJ et al., A subcellular map of the human proteome. Science. (2017) PubMed: 28495876 DOI: 10.1126/science.aal3321.
    • Sjöstedt E et al., An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. (2020) PubMed: 32139519 DOI: 10.1126/science.aay5947.
    • Karlsson M et al., A single-cell type transcriptomics map of human tissues. Sci Adv. (2021) PubMed: 34321199 DOI: 10.1126/sciadv.abh2169.
    • Uhlen M et al., A pathology atlas of the human cancer transcriptome. Science. (2017) PubMed: 28818916 DOI: 10.1126/science.aan2507.
    • Uhlen M et al., A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science. (2019) PubMed: 31857451 DOI: 10.1126/science.aax9198.
    • Sjöstedt E et al., An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. (2020) PubMed: 32139519 DOI: 10.1126/science.aay5947.
    • Uhlén M et al., The human secretome. Sci Signal. (2019) PubMed: 31772123 DOI: 10.1126/scisignal.aaz0274.
    • Becht, Etienne, et al. "Dimensionality reduction for visualizing single-cell data using UMAP." Nature biotechnology 37.1 (2019): 38-44.

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