Fixed the pandas version error in gseapy #137

omicverse/bulk/_Enrichment.py

@@ -10,173 +10,6 @@
 from ..utils import plot_text_set
 import matplotlib
 
-"""
-def enrichment_KEGG(gene_list,
-                    gene_sets=['KEGG_2019_Human'],
-                    organism='Human',
-                    description='test_name',
-                    outdir='enrichment_kegg',
-                    cutoff=0.5):
-
-    '''
-    Gene enrichment analysis of KEGG
-
-    Parameters
-    ----------
-    gene_list:list
-        The gene set to be enrichment analyzed
-    gene_sets:list
-        The gene_set of enrichr library
-        Input Enrichr Libraries (https://maayanlab.cloud/Enrichr/#stats)
-    organism:str
-        Select from (human, mouse, yeast, fly, fish, worm)
-    description:str
-        The title of enrichment
-    outdir:str
-        The savedir of enrichment
-    cutoff:float
-        Show enriched terms which Adjusted P-value < cutoff.
-
-    Returns
-    ----------
-    res:pandas.DataFrame
-        stores your last query
-    '''
-
-    enr = gp.enrichr(gene_list=gene_list,
-                 gene_sets=gene_sets,
-                 organism=organism, # don't forget to set organism to the one you desired! e.g. Yeast
-                 description=description,
-                 outdir=outdir,
-                 # no_plot=True,
-                 cutoff=cutoff # test dataset, use lower value from range(0,1)
-                )
-    subp=dotplot(enr.res2d, title=description,cmap='seismic')
-    print(subp)
-    return enr.res2d
-
-def enrichment_GO(gene_list,
-                    go_mode='Bio',
-                    organism='Human',
-                    description='test_name',
-                    outdir='enrichment_go',
-                    cutoff=0.5):
-
-    '''
-    Gene enrichment analysis of GO
-
-    Parameters
-    ----------
-    gene_list:list
-        The gene set to be enrichment analyzed
-    go_mode:str
-        The module of GO include:'Bio','Cell','Mole'
-    organism:str
-        Select from (human, mouse, yeast, fly, fish, worm)
-    description:str
-        The title of enrichment
-    outdir:str
-        The savedir of enrichment
-    cutoff:float
-        Show enriched terms which Adjusted P-value < cutoff.
-
-    Returns
-    ----------
-    result:pandas.DataFrame
-        stores your last query
-    '''
-    if(go_mode=='Bio'):
-        geneset='GO_Biological_Process_2018'
-    if(go_mode=='Cell'):
-        geneset='GO_Cellular_Component_2018'
-    if(go_mode=='Mole'):
-        geneset='GO_Molecular_Function_2018'
-    enr = gp.enrichr(gene_list=gene_list,
-                 gene_sets=geneset,
-                 organism=organism, # don't forget to set organism to the one you desired! e.g. Yeast
-                 description=description,
-                 outdir=outdir,
-                 # no_plot=True,
-                 cutoff=cutoff # test dataset, use lower value from range(0,1)
-                )
-    subp=dotplot(enr.res2d, title=description,cmap='seismic')
-    print(subp)
-    return enr.res2d
-
-def enrichment_GSEA(data,
-                   gene_sets='KEGG_2016',
-                   processes=4,
-                   permutation_num=100,
-                   outdir='prerank_report_kegg',
-                   seed=6):
-    '''
-    Gene enrichment analysis of GSEA
-
-    Parameters
-    ----------
-    data:pandas.DataFrame
-        The result of Find_DEG(function in DeGene.py)
-    gene_sets:list
-        The gene_set of enrichr library
-        Input Enrichr Libraries (https://maayanlab.cloud/Enrichr/#stats)
-    processes:int
-        CPU number
-    permutation_num:int
-        Number of permutations for significance computation. Default: 1000.
-    outdir:str
-        The savedir of enrichment
-    seed:int
-        Random seed
-
-    Returns
-    ----------
-    result:Return a Prerank obj. 
-    All results store to  a dictionary, obj.results,
-         where contains::
-
-             | {es: enrichment score,
-             |  nes: normalized enrichment score,
-             |  p: P-value,
-             |  fdr: FDR,
-             |  size: gene set size,
-             |  matched_size: genes matched to the data,
-             |  genes: gene names from the data set
-             |  ledge_genes: leading edge genes}
-    '''
-
-
-    rnk=pd.DataFrame(columns=['genename','FoldChange'])
-    rnk['genename']=data.index
-    rnk['FoldChange']=data['FoldChange'].tolist()
-    rnk1=rnk.drop_duplicates(['genename'])
-    rnk1=rnk1.sort_values(by='FoldChange', ascending=False)
-    
-    pre_res = gp.prerank(rnk=rnk1, gene_sets=gene_sets,
-                     processes=processes,
-                     permutation_num=permutation_num, # reduce number to speed up testing
-                     outdir=outdir, format='png', seed=seed)
-    pre_res.res2d.sort_index().to_csv('GSEA_result.csv')
-    return pre_res
-
-def Plot_GSEA(data,num=0):
-
-    '''
-    Plot the GSEA result figure
-
-    Parameters
-    ----------
-    data:prerank obj
-        The result of enrichment_GSEA
-    num:int
-        The sequence of pathway drawn 
-        Default:0(the first pathway)
-
-    '''
-    terms = data.res2d.index
-    from gseapy.plot import gseaplot
-    # to save your figure, make sure that ofname is not None
-    gseaplot(rank_metric=data.ranking, term=terms[num], **data.results[terms[num]])
-"""
 def geneset_enrichment(gene_list:list,pathways_dict:dict,
                        pvalue_threshold:float=0.05,pvalue_type:str='auto',
                        organism:str='Human',description:str='None',
@@ -201,14 +34,15 @@ def geneset_enrichment(gene_list:list,pathways_dict:dict,
 
 
     """
-    import gseapy as gp
+    from ..externel.gseapy import enrichr
+    #import gseapy as gp
     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'
 
-    enr = gp.enrichr(gene_list=gene_list,
+    enr = enrichr(gene_list=gene_list,
                  gene_sets=pathways_dict,
                  organism=organism, # don't forget to set organism to the one you desired! e.g. Yeast
                  description=description,
@@ -286,8 +120,8 @@ def geneset_enrichment_GSEA(gene_rnk:pd.DataFrame,pathways_dict:dict,
         pre_res: A prerank object containing the enrichment results.
     
     """
-    import gseapy as gp
-    pre_res = gp.prerank(rnk=gene_rnk, gene_sets=pathways_dict,
+    from ..externel.gseapy import prerank
+    pre_res = prerank(rnk=gene_rnk, gene_sets=pathways_dict,
                      processes=processes,
                      permutation_num=permutation_num, # reduce number to speed up testing
                      outdir=outdir, format=format, seed=seed)

omicverse/externel/gseapy/__init__.py

@@ -0,0 +1,11 @@
+#
+from .gsea import replot, prerank, gsea, ssgsea
+from .enrichr import enrichr
+from .parser import get_library_name
+from .plot import dotplot, barplot, heatmap, gseaplot
+from .__main__ import __version__
+
+
+__all__ = ['dotplot','barplot','heatmap','gseaplot', 
+           'replot','prerank','gsea','ssgsea',
+           'enrichr','get_library_name']