Different Expression Analysis¶

dds=ov.bulk.pyDEG(data)

pathway_dict=ov.utils.geneset_prepare('genesets/WikiPathways_2019_Mouse.txt',organism='Mouse')

deg_genes=dds.result.loc[dds.result['sig']!='normal'].index.tolist()
enr=ov.bulk.geneset_enrichment(gene_list=deg_genes,
                                pathways_dict=pathway_dict,
                                pvalue_type='auto',
                                organism='mouse')

pathway_dict=ov.utils.geneset_prepare('genesets/GO_Biological_Process_2023.txt',organism='Mouse')
enr_go_bp=ov.bulk.geneset_enrichment(gene_list=deg_genes,
                                pathways_dict=pathway_dict,
                                pvalue_type='auto',
                                organism='mouse')
pathway_dict=ov.utils.geneset_prepare('genesets/GO_Molecular_Function_2023.txt',organism='Mouse')
enr_go_mf=ov.bulk.geneset_enrichment(gene_list=deg_genes,
                                pathways_dict=pathway_dict,
                                pvalue_type='auto',
                                organism='mouse')
pathway_dict=ov.utils.geneset_prepare('genesets/GO_Cellular_Component_2023.txt',organism='Mouse')
enr_go_cc=ov.bulk.geneset_enrichment(gene_list=deg_genes,
                                pathways_dict=pathway_dict,
                                pvalue_type='auto',
                                organism='mouse')

def geneset_plot_multi(enr_dict,colors_dict,num:int=5,fontsize=10,
                        fig_title:str='',fig_xlabel:str='Fractions of genes',
                        figsize:tuple=(2,4),cmap:str='YlGnBu',
                        text_knock:int=5,text_maxsize:int=20,ax=None,
                        ):
    from PyComplexHeatmap import HeatmapAnnotation,DotClustermapPlotter,anno_label,anno_simple,AnnotationBase
    for key in enr_dict.keys():
        enr_dict[key]['Type']=key
    enr_all=pd.concat([enr_dict[i].iloc[:num] for i in enr_dict.keys()],axis=0)
    enr_all['Term']=[ov.utils.plot_text_set(i.split('(')[0],text_knock=text_knock,text_maxsize=text_maxsize) for i in enr_all.Term.tolist()]
    enr_all.index=enr_all.Term
    enr_all['Term1']=[i for i in enr_all.index.tolist()]
    del enr_all['Term']

    colors=colors_dict

    left_ha = HeatmapAnnotation(
                          label=anno_label(enr_all.Type, merge=True,rotation=0,colors=colors,relpos=(1,0.8)),
                          Category=anno_simple(enr_all.Type,cmap='Set1',
                                           add_text=False,legend=False,colors=colors),
                           axis=0,verbose=0,label_kws={'rotation':45,'horizontalalignment':'left','visible':False})
    right_ha = HeatmapAnnotation(
                              label=anno_label(enr_all.Term1, merge=True,rotation=0,relpos=(0,0.5),arrowprops=dict(visible=True),
                                               colors=enr_all.assign(color=enr_all.Type.map(colors)).set_index('Term1').color.to_dict(),
                                              fontsize=fontsize,luminance=0.8,height=2),
                               axis=0,verbose=0,#label_kws={'rotation':45,'horizontalalignment':'left'},
                                orientation='right')
    if ax==None:
        fig, ax = plt.subplots(figsize=figsize) 
    else:
        ax=ax
    #plt.figure(figsize=figsize)
    cm = DotClustermapPlotter(data=enr_all, x='fraction',y='Term1',value='logp',c='logp',s='num',
                              cmap=cmap,
                              row_cluster=True,#col_cluster=True,#hue='Group',
                              #cmap={'Group1':'Greens','Group2':'OrRd'},
                              vmin=-1*np.log10(0.1),vmax=-1*np.log10(1e-10),
                              #colors={'Group1':'yellowgreen','Group2':'orange'},
                              #marker={'Group1':'*','Group2':'$\\ast$'},
                              show_rownames=True,show_colnames=False,row_dendrogram=False,
                              col_names_side='top',row_names_side='right',
                              xticklabels_kws={'labelrotation': 30, 'labelcolor': 'blue','labelsize':fontsize},
                              #yticklabels_kws={'labelsize':10},
                              #top_annotation=col_ha,left_annotation=left_ha,right_annotation=right_ha,
                              left_annotation=left_ha,right_annotation=right_ha,
                              spines=False,
                              row_split=enr_all.Type,# row_split_gap=1,
                              #col_split=df_col.Group,col_split_gap=0.5,
                              verbose=1,legend_gap=10,
                              #dot_legend_marker='*',
                              
                              xlabel='Fractions of genes',xlabel_side="bottom",
                              xlabel_kws=dict(labelpad=8,fontweight='normal',fontsize=fontsize+2),
                              # xlabel_bbox_kws=dict(facecolor=facecolor)
                             )
    tesr=plt.gcf().axes
    for ax in plt.gcf().axes:
        if hasattr(ax, 'get_xlabel'):
            if ax.get_xlabel() == 'Fractions of genes':  # 假设 colorbar 有一个特定的标签
                cbar = ax
                cbar.grid(False)
            if ax.get_ylabel() == 'logp':  # 假设 colorbar 有一个特定的标签
                cbar = ax
                cbar.tick_params(labelsize=fontsize+2)
                cbar.set_ylabel(r'$−Log_{10}(P_{adjusted})$',fontsize=fontsize+2)
                cbar.grid(False)
    return ax