Api enrichment

class pyGSE(object):

    def __init__(self,gene_list:list,pathways_dict:dict,pvalue_threshold:float=0.05,pvalue_type:str='auto',
                 background=None,organism:str='Human',description:str='None',outdir:str='./enrichr',cutoff:float=0.5) -> None:
        """Initialize the pyGSEA class.

        Arguments:
            gene_list: A list of genes.
            pathways_dict: A dictionary of pathways.
            pvalue_threshold: The p-value threshold for enrichment. Default is 0.05.
            pvalue_type: The p-value type. Default is 'auto'.
            organism: The organism. Default is 'Human'.
            description: The description. Default is 'None'.
            outdir: The output directory. Default is './enrichr'.
            cutoff: The cutoff for enrichment. Default is 0.5.

        """

        self.gene_list=gene_list
        self.pathways_dict=pathways_dict
        self.pvalue_threshold=pvalue_threshold
        self.pvalue_type=pvalue_type
        self.organism=organism
        self.description=description
        self.outdir=outdir
        self.cutoff=cutoff
        if background is None:
            if (organism == 'Mouse') or (organism == 'mouse') or (organism == 'mm'):
                background='mmusculus_gene_ensembl'
            elif (organism == 'Human') or (organism == 'human') or (organism == 'hs'):
                background='hsapiens_gene_ensembl'
            self.background=background
        else:
            self.background=background

    def enrichment(self):
        """gene set enrichment analysis.

        Returns:
            A pandas.DataFrame object containing the enrichment results.
        """

        enrich_res=geneset_enrichment(self.gene_list,self.pathways_dict,self.pvalue_threshold,self.pvalue_type,
                                  self.organism,self.description,self.background,self.outdir,self.cutoff)
        self.enrich_res=enrich_res
        return enrich_res


    def plot_enrichment(self,num:int=10,node_size:list=[5,10,15],
                        cax_loc:int=2,cax_fontsize:int=12,
                        fig_title:str='',fig_xlabel:str='Fractions of genes',
                        figsize:tuple=(2,4),cmap:str='YlGnBu',text_knock:int=2,text_maxsize:int=20)->matplotlib.axes._axes.Axes:

        """Plot the gene set enrichment result.

        Arguments:
            num: The number of enriched terms to plot. Default is 10.
            node_size: A list of integers defining the size of nodes in the plot. Default is [5,10,15].
            cax_loc: The location of the colorbar on the plot. Default is 2.
            cax_fontsize: The fontsize of the colorbar label. Default is 12.
            fig_title: The title of the plot. Default is an empty string.
            fig_xlabel: The label of the x-axis. Default is 'Fractions of genes'.
            figsize: The size of the plot. Default is (2,4).
            cmap: The colormap to use for the plot. Default is 'YlGnBu'.

        Returns:
            A matplotlib.axes.Axes object.
        """
        return geneset_plot(self.enrich_res,num,node_size,cax_loc,cax_fontsize,
                            fig_title,fig_xlabel,figsize,cmap,text_knock,text_maxsize)

class pyGSEA(object):

    def __init__(self,gene_rnk:pd.DataFrame,pathways_dict:dict,
                 processes:int=8,permutation_num:int=100,
                 outdir:str='./enrichr_gsea',cutoff:float=0.5) -> None:
        """Initialize the pyGSEA class.

        Arguments:
            gene_rnk: pre-ranked correlation table or pandas DataFrame. Same input with ``GSEA`` .rnk file.
            pathways_dict: Dictionary of pathway library names and corresponding Enrichr API URLs.
            processes: Number of Processes you are going to use. Default: 8.
            permutation_num: Number of permutations for significance computation. Default: 100.
            outdir: Output directory for Enrichr results. Default is './enrichr_gsea'.
            cutoff: The cutoff for enrichment. Default is 0.5.
        """

        self.gene_rnk=gene_rnk
        self.pathways_dict=pathways_dict
        self.processes=processes
        self.permutation_num=permutation_num
        self.outdir=outdir
        self.cutoff=cutoff


    def enrichment(self,format:str='png', pval=0.05,seed:int=112)->pd.DataFrame:
        """gene set enrichment analysis.

        Arguments:
            format: Matplotlib figure format. Default: 'png'.
            seed: Random seed. Default: 112.

        Returns:
            enrich_res:A pandas.DataFrame object containing the enrichment results.
        """


        pre_res=geneset_enrichment_GSEA(self.gene_rnk,self.pathways_dict,
                                           self.processes,self.permutation_num,
                                           self.outdir,format,seed)
        self.pre_res=pre_res
        enrich_res=pre_res.res2d[pre_res.res2d['fdr']<pval]
        enrich_res['logp']=-np.log(enrich_res['fdr']+0.0001)
        enrich_res['logc']=enrich_res['nes']
        enrich_res['num']=enrich_res['matched_size']
        enrich_res['fraction']=enrich_res['matched_size']/enrich_res['geneset_size']
        enrich_res['Term']=enrich_res.index.tolist()
        enrich_res['P-value']=enrich_res['fdr']
        self.enrich_res=enrich_res
        return enrich_res

    def plot_gsea(self,term_num:int=0,
                  gene_set_title:str='',
                  figsize:tuple=(3,4),
                  cmap:str='RdBu_r',
                  title_fontsize:int=12,
                  title_y:float=0.95)->matplotlib.figure.Figure:
        """Plot the gene set enrichment result.

        Arguments:
            term_num: The number of enriched terms to plot. Default is 0.
            gene_set_title: The title of the plot. Default is an empty string.
            figsize: The size of the plot. Default is (3,4).
            cmap: The colormap to use for the plot. Default is 'RdBu_r'.
            title_fontsize: The fontsize of the title. Default is 12.
            title_y: The y coordinate of the title. Default is 0.95.

        Returns:
            fig: A matplotlib.figure.Figure object.
        """
        from gseapy.plot import GSEAPlot
        terms = self.enrich_res.index
        g = GSEAPlot(
        rank_metric=self.pre_res.ranking, term=terms[term_num],figsize=figsize,cmap=cmap,
            **self.pre_res.results[terms[term_num]]
            )
        if gene_set_title=='':
            g.fig.suptitle(terms[term_num],fontsize=title_fontsize,y=title_y)
        else:
            g.fig.suptitle(gene_set_title,fontsize=title_fontsize,y=title_y)
        g.add_axes()
        return g.fig


    def plot_enrichment(self,num:int=10,node_size:list=[5,10,15],
                        cax_loc:int=2,cax_fontsize:int=12,
                        fig_title:str='',fig_xlabel:str='Fractions of genes',
                        figsize:tuple=(2,4),cmap:str='YlGnBu',
                        text_knock:int=2,text_maxsize:int=20)->matplotlib.axes._axes.Axes:

        """Plot the gene set enrichment result.

        Arguments:
            num: The number of enriched terms to plot. Default is 10.
            node_size: A list of integers defining the size of nodes in the plot. Default is [5,10,15].
            cax_loc: The location of the colorbar on the plot. Default is 2.
            cax_fontsize: The fontsize of the colorbar label. Default is 12.
            fig_title: The title of the plot. Default is an empty string.
            fig_xlabel: The label of the x-axis. Default is 'Fractions of genes'.
            figsize: The size of the plot. Default is (2,4).
            cmap: The colormap to use for the plot. Default is 'YlGnBu'.
            text_knock: The number of terms to knock out for text labels. Default is 2.
            text_maxsize: The maximum fontsize of text labels. Default is 20.

        Returns:
            ax: A matplotlib.axes.Axes object.
        """
        return geneset_plot(self.enrich_res,num,node_size,cax_loc,cax_fontsize,
                            fig_title,fig_xlabel,figsize,cmap,text_knock,text_maxsize)