Skip to content

Api network

omicverse.bulk.pyPPI

Bases: object

Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
class pyPPI(object):

    def __init__(self,gene: list,species: int,gene_type_dict: dict,gene_color_dict: dict,
                 score: float = 0.4) -> None:
        r"""Initialize protein-protein interaction analysis.

        Arguments:
            gene: List of gene names for PPI analysis
            species: NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)
            gene_type_dict: Dictionary mapping gene names to gene types
            gene_color_dict: Dictionary mapping gene names to colors for visualization
            score: Threshold for protein interaction confidence (default: 0.4)
        """
        self.gene=gene 
        self.species=species
        self.score=score
        self.gene_type_dict=gene_type_dict
        self.gene_color_dict=gene_color_dict

    def interaction_analysis(self) -> nx.Graph:
        r"""Perform protein-protein interaction analysis.

        Returns:
            G: NetworkX Graph object containing PPI network for query genes
        """
        G=generate_G(self.gene,
                          self.species,
                          self.score)
        self.G=G 
        return G

    def plot_network(self,**kwargs) -> Tuple[matplotlib.figure.Figure,matplotlib.axes._axes.Axes]:
        r"""Plot protein-protein interaction network.

        Arguments:
            **kwargs: Additional keyword arguments passed to plot_network function

        Returns:
            fig: Figure object containing PPI network plot
            ax: Axes object containing PPI network plot
        """
        return plot_network(self.G,self.gene_type_dict,self.gene_color_dict,**kwargs)

__init__(gene, species, gene_type_dict, gene_color_dict, score=0.4)

Initialize protein-protein interaction analysis.

Parameters:

Name Type Description Default
gene list

List of gene names for PPI analysis

 required
species int

NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

 required
gene_type_dict dict

Dictionary mapping gene names to gene types

 required
gene_color_dict dict

Dictionary mapping gene names to colors for visualization

 required
score float

Threshold for protein interaction confidence (default: 0.4)

 0.4
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def __init__(self,gene: list,species: int,gene_type_dict: dict,gene_color_dict: dict,
             score: float = 0.4) -> None:
    r"""Initialize protein-protein interaction analysis.

    Arguments:
        gene: List of gene names for PPI analysis
        species: NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)
        gene_type_dict: Dictionary mapping gene names to gene types
        gene_color_dict: Dictionary mapping gene names to colors for visualization
        score: Threshold for protein interaction confidence (default: 0.4)
    """
    self.gene=gene 
    self.species=species
    self.score=score
    self.gene_type_dict=gene_type_dict
    self.gene_color_dict=gene_color_dict

interaction_analysis()

Perform protein-protein interaction analysis.

Returns:

Name Type Description
G nx.Graph

NetworkX Graph object containing PPI network for query genes
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def interaction_analysis(self) -> nx.Graph:
    r"""Perform protein-protein interaction analysis.

    Returns:
        G: NetworkX Graph object containing PPI network for query genes
    """
    G=generate_G(self.gene,
                      self.species,
                      self.score)
    self.G=G 
    return G

plot_network(**kwargs)

Plot protein-protein interaction network.

Parameters:

Name Type Description Default
**kwargs

Additional keyword arguments passed to plot_network function

 {}

Returns:

Name Type Description
fig matplotlib.figure.Figure

Figure object containing PPI network plot
ax matplotlib.axes._axes.Axes

Axes object containing PPI network plot
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def plot_network(self,**kwargs) -> Tuple[matplotlib.figure.Figure,matplotlib.axes._axes.Axes]:
    r"""Plot protein-protein interaction network.

    Arguments:
        **kwargs: Additional keyword arguments passed to plot_network function

    Returns:
        fig: Figure object containing PPI network plot
        ax: Axes object containing PPI network plot
    """
    return plot_network(self.G,self.gene_type_dict,self.gene_color_dict,**kwargs)

omicverse.bulk.string_interaction(gene, species)

Analyze protein-protein interaction network using STRING database.

Parameters:

Name Type Description Default
gene list

List of gene names for PPI analysis

 required
species int

NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

 required

Returns:

Name Type Description
res pd.DataFrame

DataFrame containing protein-protein interaction data with columns: stringId_A, stringId_B, preferredName_A, preferredName_B, ncbiTaxonId, score, nscore, fscore, pscore, ascore, escore, dscore, tscore
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def string_interaction(gene:list,species:int) -> pd.DataFrame:
    r"""Analyze protein-protein interaction network using STRING database.

    Arguments:
        gene: List of gene names for PPI analysis
        species: NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

    Returns:
        res: DataFrame containing protein-protein interaction data with columns:
             stringId_A, stringId_B, preferredName_A, preferredName_B, ncbiTaxonId,
             score, nscore, fscore, pscore, ascore, escore, dscore, tscore

    """
    import requests ## python -m pip install requests
    string_api_url = "https://string-db.org/api"
    output_format = "tsv-no-header"
    method = "network"
    request_url = "/".join([string_api_url, output_format, method])
    my_genes = gene

    params = {

        "identifiers" : "%0d".join(my_genes), # your protein
        "species" : species, # species NCBI identifier 
        "caller_identity" : "www.awesome_app.org" # your app name

    }
    response = requests.post(request_url, data=params)
    res=pd.DataFrame()
    res['stringId_A']=[j.strip().split("\t")[0] for j in response.text.strip().split("\n")]
    res['stringId_B']=[j.strip().split("\t")[1] for j in response.text.strip().split("\n")]
    res['preferredName_A']=[j.strip().split("\t")[2] for j in response.text.strip().split("\n")]
    res['preferredName_B']=[j.strip().split("\t")[3] for j in response.text.strip().split("\n")]
    res['ncbiTaxonId']=[j.strip().split("\t")[4] for j in response.text.strip().split("\n")]
    res['score']=[j.strip().split("\t")[5] for j in response.text.strip().split("\n")]
    res['nscore']=[j.strip().split("\t")[6] for j in response.text.strip().split("\n")]
    res['fscore']=[j.strip().split("\t")[7] for j in response.text.strip().split("\n")]
    res['pscore']=[j.strip().split("\t")[8] for j in response.text.strip().split("\n")]
    res['ascore']=[j.strip().split("\t")[9] for j in response.text.strip().split("\n")]
    res['escore']=[j.strip().split("\t")[10] for j in response.text.strip().split("\n")]
    res['dscore']=[j.strip().split("\t")[11] for j in response.text.strip().split("\n")]
    res['tscore']=[j.strip().split("\t")[12] for j in response.text.strip().split("\n")]
    return res

omicverse.bulk.string_map(gene, species)

Map gene names to STRING database identifiers.

Parameters:

Name Type Description Default
gene list

List of gene names for PPI analysis

 required
species int

NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

 required

Returns:

Name Type Description
res pd.DataFrame

DataFrame containing gene mapping information with columns: queryItem, queryIndex, stringId, ncbiTaxonId, taxonName, preferredName, annotation
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def string_map(gene:list,species:int)->pd.DataFrame:
    r"""Map gene names to STRING database identifiers.

    Arguments:
        gene: List of gene names for PPI analysis
        species: NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

    Returns:
        res: DataFrame containing gene mapping information with columns:
             queryItem, queryIndex, stringId, ncbiTaxonId, taxonName,
             preferredName, annotation

    """
    import requests ## python -m pip install requests
    string_api_url = "https://string-db.org/api"
    output_format = "tsv-no-header"
    method = "get_string_ids"
    params = {

        "identifiers" : "\r".join(gene), # your protein list
        "species" : species, # species NCBI identifier 
        "limit" : 1, # only one (best) identifier per input protein
        "echo_query" : 1, # see your input identifiers in the output
        "caller_identity" : "www.awesome_app.org" # your app name

    }
    request_url = "/".join([string_api_url, output_format, method])
    response = requests.post(request_url, data=params)
    res=pd.DataFrame(columns=['queryItem','queryIndex','stringId','ncbiTaxonId','taxonName','preferredName','annotation'])
    res['queryItem']=[j.strip().split("\t")[0] for j in response.text.strip().split("\n")]
    res['queryIndex']=[j.strip().split("\t")[1] for j in response.text.strip().split("\n")]
    res['stringId']=[j.strip().split("\t")[2] for j in response.text.strip().split("\n")]
    res['ncbiTaxonId']=[j.strip().split("\t")[3] for j in response.text.strip().split("\n")]
    res['taxonName']=[j.strip().split("\t")[4] for j in response.text.strip().split("\n")]
    res['preferredName']=[j.strip().split("\t")[5] for j in response.text.strip().split("\n")]
    res['annotation']=[j.strip().split("\t")[6] for j in response.text.strip().split("\n")]

    return res

omicverse.bulk.generate_G(gene, species, score=0.4)

Generate protein-protein interaction network from STRING database.

Parameters:

Name Type Description Default
gene list

List of gene names for PPI analysis

 required
species int

NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)

 required
score float

Threshold for protein interaction confidence (default: 0.4)

 0.4

Returns:

Name Type Description
G nx.Graph

NetworkX Graph object containing PPI network
Source code in /Users/fernandozeng/miniforge3/envs/space/lib/python3.10/site-packages/omicverse/bulk/_network.py 
def generate_G(gene:list,species:int,score:float=0.4) -> nx.Graph:
    r"""Generate protein-protein interaction network from STRING database.

    Arguments:
        gene: List of gene names for PPI analysis
        species: NCBI taxon identifiers (e.g. Human is 9606, see STRING organisms)
        score: Threshold for protein interaction confidence (default: 0.4)

    Returns:
        G: NetworkX Graph object containing PPI network

    """

    a=string_interaction(gene,species)
    b=a.drop_duplicates()
    b.head()
    G = nx.Graph()
    G.add_nodes_from(set(b['preferredName_A'].tolist()+b['preferredName_B'].tolist()))

    #Connect nodes
    for i in b.index:
        col_label = b.loc[i]['preferredName_A']
        row_label = b.loc[i]['preferredName_B']
        if(float(b.loc[i]['score'])>score):
            G.add_edge(col_label,row_label)
    return G