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omicverse.bulk.pyPPI

Bases: object

Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/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:
        """Initialize the protein-protein interaction analysis.

        Arguments:
            gene: The gene list to analysis PPI
            species: NCBI taxon identifiers (e.g. Human is 9606, see: STRING organisms).
            gene_type_dict: The gene type dict, the key is gene name, the value is gene type.
            gene_color_dict: The gene color dict, the key is gene name, the value is gene color.
            score: The threshold of protein A and B interaction
        """
        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:
        """Analysis the protein-protein interaction.

        Returns:
            G: the networkx object of PPI in query gene list
        """
        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]:
        """Plot the protein-protein interaction network.

        Returns:
            fig: the figure of PPI network
            ax: the AxesSubplot of PPI network
        """
        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 the protein-protein interaction analysis.

Parameters:

Name Type Description Default
gene list

The gene list to analysis PPI

 required
species int

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

 required
gene_type_dict dict

The gene type dict, the key is gene name, the value is gene type.

 required
gene_color_dict dict

The gene color dict, the key is gene name, the value is gene color.

 required
score float

The threshold of protein A and B interaction

 0.4
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/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:
    """Initialize the protein-protein interaction analysis.

    Arguments:
        gene: The gene list to analysis PPI
        species: NCBI taxon identifiers (e.g. Human is 9606, see: STRING organisms).
        gene_type_dict: The gene type dict, the key is gene name, the value is gene type.
        gene_color_dict: The gene color dict, the key is gene name, the value is gene color.
        score: The threshold of protein A and B interaction
    """
    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()

Analysis the protein-protein interaction.

Returns:

Name Type Description
G nx.Graph

the networkx object of PPI in query gene list
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/omicverse/bulk/_network.py 
def interaction_analysis(self) -> nx.Graph:
    """Analysis the protein-protein interaction.

    Returns:
        G: the networkx object of PPI in query gene list
    """
    G=generate_G(self.gene,
                      self.species,
                      self.score)
    self.G=G 
    return G

plot_network(**kwargs)

Plot the protein-protein interaction network.

Returns:

Name Type Description
fig matplotlib.figure.Figure

the figure of PPI network
ax matplotlib.axes._axes.Axes

the AxesSubplot of PPI network
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/omicverse/bulk/_network.py 
def plot_network(self,**kwargs) -> Tuple[matplotlib.figure.Figure,matplotlib.axes._axes.Axes]:
    """Plot the protein-protein interaction network.

    Returns:
        fig: the figure of PPI network
        ax: the AxesSubplot of PPI network
    """
    return plot_network(self.G,self.gene_type_dict,self.gene_color_dict,**kwargs)

omicverse.bulk.string_interaction(gene, species)

A Python library to analysis the protein-protein interaction network by string-db

Parameters:

Name Type Description Default
gene list

The gene list to analysis PPI

 required
species int

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

 required

Returns:

Name Type Description
res pd.DataFrame

the dataframe of protein-protein interaction
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/omicverse/bulk/_network.py 
def string_interaction(gene:list,species:int) -> pd.DataFrame:
    r"""A Python library to analysis the protein-protein interaction network by string-db

    Arguments:
        gene: The gene list to analysis PPI
        species: NCBI taxon identifiers (e.g. Human is 9606, see: STRING organisms).

    Returns:
        res: the dataframe of protein-protein interaction

    """
    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)

A Python library to find the gene name in string-db

Parameters:

Name Type Description Default
gene list

The gene list to analysis PPI

 required
species int

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

 required

Returns:

Name Type Description
res pd.DataFrame

the dataframe of query gene and new gene
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/omicverse/bulk/_network.py 
def string_map(gene:list,species:int)->pd.DataFrame:
    r"""A Python library to find the gene name in string-db

    Arguments:
        gene: The gene list to analysis PPI
        species: NCBI taxon identifiers (e.g. Human is 9606, see: STRING organisms).

    Returns:
        res: the dataframe of query gene and new gene

    """
    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)

A Python library to get the PPI network in string-db

Parameters:

Name Type Description Default
gene list

The gene list to analysis PPI

 required
species int

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

 required
score float

The threshold of protein A and B interaction

 0.4

Returns:

Name Type Description
G nx.Graph

the networkx object of PPI in query gene list
Source code in /Users/fernandozeng/miniforge3/envs/scbasset/lib/python3.8/site-packages/omicverse/bulk/_network.py 
def generate_G(gene:list,species:int,score:float=0.4) -> nx.Graph:
    r"""A Python library to get the PPI network in string-db

    Arguments:
        gene: The gene list to analysis PPI
        species: NCBI taxon identifiers (e.g. Human is 9606, see: STRING organisms).
        score: The threshold of protein A and B interaction

    Returns:
        G: the networkx object of PPI in query gene list

    """

    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