Upload app.py

app.py

@@ -60,21 +60,21 @@ cwd=os.getcwd()+'/'#+'data/'
 def get_data():
     #if 'adata_annot' not in st.session_state or 'cell_type' not in st.session_state or 'broad_type' not in st.session_state:
     adata_annot = sc.read_h5ad(cwd+'multiregion_brainaging_annotated.h5ad')
-    st.session_state['adata_annot'] = adata_annot
-    if 'genes_list' not in st.session_state:        
-        genes=adata_annot.var.index
-        #genes_list=sorted(genes.unique())
-        st.session_state['genes_list'] = sorted(genes.unique())
-    if 'cell_type' not in st.session_state:
+    full_adata_annot = adata_annot
+    #if 'genes_list' not in st.session_state:        
+    genes=adata_annot.var.index
+    #genes_list=sorted(genes.unique())
+    all_genes_list = sorted(genes.unique())
+    #if 'cell_type' not in st.session_state:
         #cell_type=diff_fdr[diff_fdr.type=='cell_type']['tissue']
         #cell_type=sorted(cell_type.unique())
-        anno=adata_annot.obs.new_anno
-        #cell_type=sorted(anno.unique())
-        st.session_state['cell_type'] = sorted(anno.unique())
-    if 'broad_type' not in st.session_state:        
-        broad_celltype=adata_annot.obs.broad_celltype
-        #broad_type=sorted(broad_type.unique())
-        st.session_state['broad_type'] = sorted(broad_celltype.unique())
+    anno=adata_annot.obs.new_anno
+    #cell_type=sorted(anno.unique())
+    all_celltype_list = sorted(anno.unique())
+    #if 'broad_type' not in st.session_state:        
+    broad_celltype=adata_annot.obs.broad_celltype
+    #broad_type=sorted(broad_type.unique())
+    #st.session_state['broad_type'] = sorted(broad_celltype.unique())
             
     #Also load Go Terms
 #    if 'go_table' not in st.session_state:        
@@ -87,8 +87,10 @@ def get_data():
 
     gene_set_by_path = {key: [val for val in value.values() if val != ""] for key, value in go_bp_paths_dict.items()}
     gene_set_by_path = pd.DataFrame.from_dict(gene_set_by_path, orient='index').transpose()     
-    st.session_state['path_ways']=gene_set_by_path.columns
-    st.session_state['go_table']=gene_set_by_path
+    all_path_ways=gene_set_by_path.columns
+    full_go_table=gene_set_by_path
+    
+    return full_adata_annot,all_genes_list,all_celltype_list,broad_celltype,all_path_ways,full_go_table
 #done load Data
 
 
@@ -98,7 +100,7 @@ st.title('Brain Age Browser')
 
 #genes_list,adata_annot=get_data()
 
-get_data()
+full_adata_annot,all_genes_list,all_celltype_list,broad_celltype,all_path_ways,full_go_table=get_data()
 
 tab1, tab2,readme = st.tabs(["Gene Expression by CellType", "Age associations for multiple genes", "README"])
 data = np.random.randn(10, 1)
@@ -109,11 +111,11 @@ with tab1:
         with c1:
             selected_gene = st.selectbox(
             'Please select a gene',
-            st.session_state['genes_list'])
+            all_genes_list)
         with c2:
             selected_celltype = st.selectbox(
             'Please select CellType',
-            st.session_state['cell_type']
+            all_celltype_list
             )
         Updated=st.form_submit_button(label = 'Go')
     if not isinstance(selected_gene, type(None)) and not isinstance(selected_celltype, type(None)) and Updated:
@@ -122,13 +124,13 @@ with tab1:
         col1,col2= st.columns([1,1])
         with col1:
             fig11, axx11 = plt.subplots(figsize=(5,5))
-            sc.pl.umap(st.session_state['adata_annot'], color='new_anno', title='', legend_loc='on data',legend_fontsize='8', frameon=False,show=False, ax=axx11)
+            sc.pl.umap(full_adata_annot, color='new_anno', title='', legend_loc='on data',legend_fontsize='8', frameon=False,show=False, ax=axx11)
             st.pyplot(fig11)   
                  
         with col2:
             fig12, axx12 = plt.subplots(figsize=(5,5))
-            #sc.pl.umap(st.session_state['adata_annot'], color='new_anno', title='', legend_loc='on data', frameon=False,show=False, ax=axx2)
-            sc.pl.umap(st.session_state['adata_annot'], color=selected_gene, title='', legend_loc='best', frameon=False,show=False,legend_fontsize='xx-small', ax=axx12)#,vmax='p99')
+            #sc.pl.umap(full_adata_annot, color='new_anno', title='', legend_loc='on data', frameon=False,show=False, ax=axx2)
+            sc.pl.umap(full_adata_annot, color=selected_gene, title='', legend_loc='best', frameon=False,show=False,legend_fontsize='xx-small', ax=axx12)#,vmax='p99')
             #plt.xticks(rotation = 45)
             #plt.colorbar(cax=cax)
             axx12.set_title(selected_gene, fontsize=12)
@@ -136,8 +138,8 @@ with tab1:
             st.pyplot(fig12)        
 
         #Subset Younv and Old
-        adata_Young = st.session_state['adata_annot'][st.session_state['adata_annot'].obs['Age_group']=='young']
-        adata_Old = st.session_state['adata_annot'][st.session_state['adata_annot'].obs['Age_group']=='old']
+        adata_Young = full_adata_annot[full_adata_annot.obs['Age_group']=='young']
+        adata_Old = full_adata_annot[full_adata_annot.obs['Age_group']=='old']
         
         #Young/Old  but for cell_type
         adata_YoungAst = adata_Young[adata_Young.obs['new_anno']==selected_celltype]
@@ -192,11 +194,11 @@ with tab2:
         with c1:
             multi_genes = st.multiselect(
             'Select Genes List',
-            st.session_state['genes_list'])
+            all_genes_list)
         with c2:
             go_term = st.selectbox(
             'Select GO Term',
-            st.session_state['path_ways'])
+            all_path_ways)
         with c3:
             Choice = st.radio(
             "",
@@ -207,13 +209,13 @@ with tab2:
         if Choice=='Gene Set':
             multi_genes = np.sort(multi_genes)
         else:
-            multi_genes=st.session_state['go_table'].loc[:,go_term]
+            multi_genes=full_go_table.loc[:,go_term]
             multi_genes=multi_genes.dropna().values
             multi_genes=np.sort(multi_genes)
         #multi_genes=['WNT3', 'VPS13C', 'VAMP4', 'UBTF', 'UBAP2', 'TMEM175', 'TMEM163', 'SYT17', 'STK39', 'SPPL2B', 'SIPA1L2', 'SH3GL2', 'SCARB2', 'SCAF11', 'RPS6KL1', 'RPS12', 'RIT2', 'RIMS1', 'RETREG3', 'PMVK', 'PAM', 'NOD2', 'MIPOL1', 'MEX3C', 'MED12L', 'MCCC1', 'MBNL2', 'MAPT', 'LRRK2', 'KRTCAP2', 'KCNS3', 'KCNIP3', 'ITGA8', 'IP6K2', 'GPNMB', 'GCH1', 'GBA', 'FYN', 'FCGR2A', 'FBRSL1', 'FAM49B', 'FAM171A2', 'ELOVL7', 'DYRK1A', 'DNAH17', 'DLG2', 'CTSB', 'CRLS1', 'CRHR1', 'CLCN3', 'CHRNB1', 'CAMK2D', 'CAB39L', 'BRIP1', 'BIN3', 'ASXL3', 'SNCA']
         ######### THIS IS FOR CLUSTERMAP
             # figxx = plt.subplots(figsize=(5, 5))
-            # hmpdat=st.session_state['adata_annot'][:, multi_genes] #.to_df()
+            # hmpdat=full_adata_annot[:, multi_genes] #.to_df()
             # #st.write(hmpdat)
             # samples=hmpdat.obs.new_anno
             # dfh = pd.DataFrame(hmpdat.X.toarray(), columns = multi_genes)
@@ -224,21 +226,21 @@ with tab2:
         ######
         
         #col1,col2= st.columns([1,1])
-        #fig_szx=2*len(st.session_state['cell_type'])
+        #fig_szx=2*len(all_celltype_list)
         #fig_szy=100*len(multi_genes)
         #with col1:
         fig11, axx11 = plt.subplots(figsize=(5, 5))
-        #sc.pl.umap(st.session_state['adata_annot'], color='new_anno', title='', legend_loc='on data',legend_fontsize='8', frameon=False,show=False, ax=axx11)
-        axx11=sc.pl.dotplot(st.session_state['adata_annot'], multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='BuPu',swap_axes=True,show=False,vmax=5)
+        #sc.pl.umap(full_adata_annot, color='new_anno', title='', legend_loc='on data',legend_fontsize='8', frameon=False,show=False, ax=axx11)
+        axx11=sc.pl.dotplot(full_adata_annot, multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='BuPu',swap_axes=True,show=False,vmax=5)
         #st.pyplot(fig11)      
         #st.pyplot(plt.gcf().set_size_inches(fig_szx, fig_szy))  
         st.pyplot(plt.gcf())  
 #        with col2:
         fig12, axx12 = plt.subplots(figsize=(5, 5))
         
-        #sc.pl.umap(st.session_state['adata_annot'], color='new_anno', title='', legend_loc='on data', frameon=False,show=False, ax=axx2)
-        #sc.pl.umap(st.session_state['adata_annot'], color=selected_gene, title=selected_gene, legend_loc='best', frameon=False,show=False,legend_fontsize='xx-small', ax=axx12)#,vmax='p99')
-        axx12=sc.pl.heatmap(st.session_state['adata_annot'], multi_genes, groupby='new_anno', vmin=-1, vmax=1, cmap='BuPu', dendrogram=True, swap_axes=True, show_gene_labels=True,var_group_rotation=45)#,ax=ax2)
+        #sc.pl.umap(full_adata_annot, color='new_anno', title='', legend_loc='on data', frameon=False,show=False, ax=axx2)
+        #sc.pl.umap(full_adata_annot, color=selected_gene, title=selected_gene, legend_loc='best', frameon=False,show=False,legend_fontsize='xx-small', ax=axx12)#,vmax='p99')
+        axx12=sc.pl.heatmap(full_adata_annot, multi_genes, groupby='new_anno', vmin=-1, vmax=1, cmap='BuPu', dendrogram=True, swap_axes=True, show_gene_labels=True,var_group_rotation=45)#,ax=ax2)
         plt.xticks(rotation = 45)
         #plt.xticks(rotation = 45)
         #st.pyplot(fig12)      
@@ -250,12 +252,12 @@ with tab2:
         #multi_genes=['WNT3', 'VPS13C', 'VAMP4', 'UBTF', 'UBAP2', 'TMEM175', 'TMEM163', 'SYT17', 'STK39', 'SPPL2B', 'SIPA1L2', 'SH3GL2', 'SCARB2', 'SCAF11', 'RPS6KL1', 'RPS12', 'RIT2', 'RIMS1', 'RETREG3', 'PMVK', 'PAM', 'NOD2', 'MIPOL1', 'MEX3C', 'MED12L', 'MCCC1', 'MBNL2', 'MAPT', 'LRRK2', 'KRTCAP2', 'KCNS3', 'KCNIP3', 'ITGA8', 'IP6K2', 'GPNMB', 'GCH1', 'GBA', 'FYN', 'FCGR2A', 'FBRSL1', 'FAM49B', 'FAM171A2', 'ELOVL7', 'DYRK1A', 'DNAH17', 'DLG2', 'CTSB', 'CRLS1', 'CRHR1', 'CLCN3', 'CHRNB1', 'CAMK2D', 'CAB39L', 'BRIP1', 'BIN3', 'ASXL3', 'SNCA']
         #multi_genes=np.sort(multi_genes)
         # fig, ax1 = plt.subplots(1,2)
-        # sc.pl.dotplot(st.session_state['adata_annot'], multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',show=False, ax=ax1[0])
+        # sc.pl.dotplot(full_adata_annot, multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',show=False, ax=ax1[0])
         # st.pyplot(plt.gcf().set_size_inches(10, 10))
         # fig, ax2 = plt.subplots(1,2)
-        # ax2=sc.pl.heatmap(st.session_state['adata_annot'], multi_genes, 'new_anno', vmin=-1, vmax=1, cmap='RdBu_r', dendrogram=True, swap_axes=True)
+        # ax2=sc.pl.heatmap(full_adata_annot, multi_genes, 'new_anno', vmin=-1, vmax=1, cmap='RdBu_r', dendrogram=True, swap_axes=True)
         # st.pyplot(plt.gcf().set_size_inches(10, 10))
-        #ax[0]=sc.pl.dotplot(st.session_state['adata_annot'],multi_genes,'new_anno',show=False)
+        #ax[0]=sc.pl.dotplot(full_adata_annot,multi_genes,'new_anno',show=False)
         #fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,4), gridspec_kw={'wspace':0.9})
      
      
@@ -264,15 +266,15 @@ with tab2:
      
         # fig, (ax1) = plt.subplots(1, 1, figsize=(20,4), gridspec_kw={'wspace':0.9})
         # #ax = plt.subplot()
-        # ax1_dict=sc.pl.dotplot(st.session_state['adata_annot'], multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='BuPu',swap_axes=True,show=False, ax=ax1,vmax=5)
-        # #ax_dict=sc.pl.dotplot(st.session_state['adata_annot'], multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',swap_axes=True,show=False, ax=ax)
+        # ax1_dict=sc.pl.dotplot(full_adata_annot, multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='BuPu',swap_axes=True,show=False, ax=ax1,vmax=5)
+        # #ax_dict=sc.pl.dotplot(full_adata_annot, multi_genes,'new_anno',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',swap_axes=True,show=False, ax=ax)
         # st.pyplot(plt.gcf().set_size_inches(10, 15))
-        # #ax2_dict=sc.pl.dotplot(st.session_state['adata_annot'], multi_genes,'Sex',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',swap_axes=True,show=False, ax=ax2)
+        # #ax2_dict=sc.pl.dotplot(full_adata_annot, multi_genes,'Sex',size_title='Fraction of\n Expressing Cells',colorbar_title='Mean\nExpression',cmap='RdBu_r',swap_axes=True,show=False, ax=ax2)
         # fig, (ax2) = plt.subplots(1, 1, figsize=(20,4), gridspec_kw={'wspace':0.9})
-        # #ax2_dict=sc.pl.matrixplot(st.session_state['adata_annot'], multi_genes, 'new_anno', vmin=-1, vmax=1, show=False, cmap='BuPu',dendrogram=True, swap_axes=True, ax=ax2)
+        # #ax2_dict=sc.pl.matrixplot(full_adata_annot, multi_genes, 'new_anno', vmin=-1, vmax=1, show=False, cmap='BuPu',dendrogram=True, swap_axes=True, ax=ax2)
         
         # #sc.pl.heatmap(adata_annot, genes_lst, groupby='new_anno', vmin=-1, vmax=1, cmap='RdBu_r', dendrogram=True, swap_axes=True, figsize=(11,4))
-        # ax2_dict=sc.pl.heatmap(st.session_state['adata_annot'], multi_genes, groupby='new_anno', vmin=-1, vmax=1, cmap='BuPu', dendrogram=True, swap_axes=True)#,ax=ax2)
+        # ax2_dict=sc.pl.heatmap(full_adata_annot, multi_genes, groupby='new_anno', vmin=-1, vmax=1, cmap='BuPu', dendrogram=True, swap_axes=True)#,ax=ax2)
         
         # st.pyplot(plt.gcf().set_size_inches(10, 15))