update omicverse to 1.3.1

### single module

- add `get_celltype_marker` method in `single`
- add `GLUE_pair`, `pyMOFA`, `pyMOFAART` module in `single`
- add tutorial of `Multi omics analysis by MOFA and GLUE`
- update tutorial of `Multi omics analysis by MOFA`

Author: Starlitnightly

.DS_Store

@@ -671,6 +671,7 @@ def set_train_options(self,
         if gpu_mode:
             try:
                 import cupy as cp
+
                 print("\nGPU mode is activated\n")
             except ImportError:
                 print("\nGPU mode is activated, but GPU not found... switching to CPU mode")

omicverse/mofapy2/run/entry_point.py

@@ -3,9 +3,9 @@
 """
 
 from ._cosg import cosg
-from ._anno import pySCSA,scanpy_lazy,scanpy_cellanno_from_dict
+from ._anno import pySCSA,scanpy_lazy,scanpy_cellanno_from_dict,get_celltype_marker
 from ._nocd import scnocd
-from ._mofa import mofa,factor_exact,factor_correlation,get_weights
+from ._mofa import pyMOFAART,pyMOFA,GLUE_pair,factor_exact,factor_correlation,get_weights,glue_pair
 from ._scdrug import autoResolution,writeGEP,Drug_Response
 from ._cpdb import cpdb_network_cal,cpdb_plot_network,cpdb_plot_interaction,cpdb_interaction_filtered,cpdb_submeans_exacted
 from ._scgsea import geneset_aucell,pathway_aucell,pathway_aucell_enrichment,pathway_enrichment,pathway_enrichment_plot

omicverse/single/__init__.py

@@ -275,6 +275,38 @@ def scanpy_cellanno_from_dict(adata:anndata.AnnData,
     adata.obs[anno_name+'_celltype'] = adata.obs[clustertype].map(anno_dict).astype('category')
     print('...cell type added to {}_celltype on obs of anndata'.format(anno_name))
 
+def get_celltype_marker(adata:anndata.AnnData,
+                            clustertype:str='leiden',
+                            log2fc_min:int=2,scores_type='scores',
+                            pval_cutoff:float=0.05,rank:bool=False)->dict:
+        r"""Get marker genes for each clusters.
+        
+        Arguments:
+            adata: anndata object
+            clustertype: Clustering name used in scanpy. (leiden)
+            log2fc_min: Minimum log2 fold change of marker genes. (2)
+            pval_cutoff: Maximum p value of marker genes. (0.05)
+            rank: Whether to rank genes by wilcoxon test. (True)
+            scores_type: The type of scores. can be selected from `scores` and `logfoldchanges`
+
+        Returns:
+            cellmarker: A dictionary of marker genes for each clusters.
+        """
+        print('...get cell type marker')
+        celltypes = sorted(adata.obs[clustertype].unique())
+        cell_marker_dict={}
+        if rank==False:
+            sc.tl.rank_genes_groups(adata, clustertype, method='wilcoxon')
+        for celltype in celltypes:
+            degs = sc.get.rank_genes_groups_df(adata, group=celltype, key='rank_genes_groups', log2fc_min=log2fc_min, 
+                                            pval_cutoff=pval_cutoff)
+            foldp=np.histogram(degs[scores_type])
+            foldchange=(foldp[1][np.where(foldp[1]>0)[0][-5]]+foldp[1][np.where(foldp[1]>0)[0][-6]])/2
+            
+            cellmarker=degs.loc[degs[scores_type]>foldchange]['names'].values
+            cell_marker_dict[celltype]=cellmarker
+
+        return cell_marker_dict
 
 class pySCSA(object):
 
@@ -433,7 +465,7 @@ def cell_auto_anno(self,adata:anndata.AnnData,clustertype:str='leiden')->None:
 
     def get_celltype_marker(self,adata:anndata.AnnData,
                             clustertype:str='leiden',
-                            log2fc_min:int=2,
+                            log2fc_min:int=2,scores_type='scores',
                             pval_cutoff:float=0.05,rank:bool=True)->dict:
         r"""Get marker genes for each clusters.
         
@@ -443,22 +475,15 @@ def get_celltype_marker(self,adata:anndata.AnnData,
             log2fc_min: Minimum log2 fold change of marker genes. (2)
             pval_cutoff: Maximum p value of marker genes. (0.05)
             rank: Whether to rank genes by wilcoxon test. (True)
+            scores_type: The type of scores. can be selected from `scores` and `logfoldchanges`
 
         Returns:
             cellmarker: A dictionary of marker genes for each clusters.
         """
         print('...get cell type marker')
-        celltypes = sorted(adata.obs[clustertype].unique())
-        cell_marker_dict={}
-        if rank==False and 'rank_genes_groups' in adata.uns.keys():
-            sc.tl.rank_genes_groups(adata, clustertype, method='wilcoxon')
-        for celltype in celltypes:
-            degs = sc.get.rank_genes_groups_df(adata, group=celltype, key='rank_genes_groups', log2fc_min=log2fc_min, 
-                                            pval_cutoff=pval_cutoff)
-            foldp=np.histogram(degs['scores'])
-            foldchange=(foldp[1][np.where(foldp[1]>0)[0][-5]]+foldp[1][np.where(foldp[1]>0)[0][-6]])/2
-            
-            cellmarker=degs.loc[degs['scores']>foldchange]['names'].values
-            cell_marker_dict[celltype]=cellmarker
+        cell_marker_dict=get_celltype_marker(adata=adata,
+                            clustertype=clustertype,
+                            log2fc_min=log2fc_min,scores_type=scores_type,
+                            pval_cutoff=pval_cutoff,rank=rank)
 
         return cell_marker_dict