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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+megan_pathways/GO_Biological_Process_2021.csv filter=lfs diff=lfs merge=lfs -text
+new_multiregion_brainaging_annotated.h5ad filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.9.12
+
+WORKDIR /code
+
+#RUN pip install --upgrade pip
+#RUN apt-get update && apt-get install build-essential -y
+#RUN apt-get update && apt-get install default-jdk -y
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+EXPOSE 8080
+
+COPY . .
+
+CMD ["streamlit", "run", "/code/app.py","address","0.0.0.0","--port", "7860","--allow-websocket-origin", "syedislamuddin-agexp.hf.space"]
+

app.py

@@ -0,0 +1,512 @@
+#All Plots in THIS APP ARE BASED ON pegasus (As opposed to scanpy in previous versions)
+import os
+import numpy as np
+import streamlit as st
+#from st_aggrid import AgGrid, GridOptionsBuilder,GridUpdateMode,DataReturnMode
+
+#import scanpy as sc
+import pegasus as pg
+from pandas import read_csv, pivot
+import seaborn as sns
+from seaborn import clustermap
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import rc_context
+from matplotlib import rcParams
+#import matplotlib as mpl
+#import pandas as pd
+#import matplotlib.font_manager as fm
+
+
+
+
+import matplotlib.pyplot as plt
+
+plt.rcParams.update({'figure.autolayout': True})
+plt.rcParams['axes.linewidth'] = 0.001
+
+
+#from functions import pathway_analyses
+
+#sc.settings.set_figure_params(dpi=80, facecolor='white',fontsize=12)
+@st.cache_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 or 'cluster_table' not in st.session_state or 'go_table' not in st.session_state or 'all_pwaydata' not in st.session_state:
+        adata_annot = pg.read_input(cwd+'new_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:
+            anno=adata_annot.obs.new_anno
+            st.session_state['cell_type'] = sorted(anno.unique())
+            
+    #Also load Go Terms
+    if 'go_table' not in st.session_state or 'all_pwaydata' not in st.session_state:  
+        
+        ###new
+        #All pathways database
+        go_table=['Aging_Perturbations_from_GEO_DOWN','Aging_Perturbations_from_GEO_UP','Disease_Perturbations_from_GEO_down','Disease_Perturbations_from_GEO_up','GO_Biological_Process_2021','GO_Cellular_Component_2021','GO_Molecular_Function_2021','KEGG 2021 Human','Wiki 2021 Human']
+        all_pwaydata={}
+        #get pathways
+        import pandas as pd
+        for f in go_table:
+            all_pwaydata[f]=pd.read_csv("megan_pathways/"+f+".csv")
+        st.session_state['go_table']=go_table
+        st.session_state['all_pwaydata']=all_pwaydata
+        ###new
+        #Also get clustermap data set
+    if 'cluster_table' not in st.session_state:        
+        st.session_state['cluster_table'] = pd.read_csv('aging.glmmtmb_age_diffs_fdr.csv',index_col=0)
+#done load Data
+#disable st.pyplot warning
+st.set_page_config(layout="wide") 
+st.markdown(
+    """
+<style>
+.streamlit-expanderHeader {
+    font-size: x-large;
+}
+</style>
+""",
+    unsafe_allow_html=True,
+)
+m=st.markdown("""
+
+<style>
+
+div.stTitle {
+
+font-size:40px;
+
+}
+
+</style>"""
+,unsafe_allow_html=True)
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+#load Data
+cwd=os.getcwd()+'/'#+'data/'
+
+def convert_df(df):
+    return df.to_csv().encode('utf-8')
+
+def disp_table(data_table): 
+    if data_table.shape[0]>0:
+        #df = transform(data_table,'Please Select columns to save whole table')
+        #fname = st_keyup("Please input file name to save Table", value='temp') #st.text_input('Please input file name to save Table', 'temp', live=True)
+        
+        csv = convert_df(data_table)
+
+        st.download_button(
+            label="Download Table as CSV file",
+            data=csv,
+            #file_name=fname+'.csv',
+            file_name='download_gene_list.csv',
+            mime='text/csv',
+        )
+
+
+
+#st.title('Single nuclei atlas of human aging in brain regions')
+
+get_data()
+st.title('Brain Age Browser')
+txt="In the event of APP CRASH, Please Press Reset Button below"
+st.markdown(f'<p style=color:red;font-size:24px;border-radius:2%;">{txt}</p>', unsafe_allow_html=True)
+
+#st.header("In the event of **APP CRASH**, Please Press Reset Button below")
+m = st.markdown("""
+<style>
+div.stButton > button:first-child {
+    background-color: #0099ff;
+    color:#ffffff;
+}
+div.stButton > button:hover {
+    background-color: #00ff00;
+    color:#ff0000;
+    }
+</style>""", unsafe_allow_html=True)
+
+#b = st.button("点我开始运行程序")
+clear=st.button('Reset')
+if clear:
+    st.cache_data.clear()
+    #st.runtime.legacy_caching.clear_cache()
+
+#tab1, tab2,readme = st.tabs(["Gene Expression by CellType", "Age associations for multiple genes", "README"])
+st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: center;} </style>', unsafe_allow_html=True)
+
+opt_selected = st.radio(
+    "**Please select an option**",
+    ("Gene Expression by CellType", "Age associations for Multiple genes", "Age associations with GoTerms", "README"))
+
+if opt_selected == 'Gene Expression by CellType':
+#tab1, tab2,tab3,readme = st.tabs(["Gene Expression by CellType", "Age associations for Multiple genes", "Age associations with GoTerms", "README"])
+#data = np.random.randn(10, 1)
+#with tab1:
+    with st.form(key='columns_in_form'):
+        #c1, c2, c3 = st.columns([4,4,2])
+        c1, c2 = st.columns(2)
+        with c1:
+            selected_gene = st.selectbox(
+            'Please select a gene',
+            st.session_state['genes_list'])
+        with c2:
+            selected_celltype = st.selectbox(
+            'Please select CellType',
+            st.session_state['cell_type']
+            )
+        Updated=st.form_submit_button(label = 'Go')
+    if not isinstance(selected_gene, type(None)) and not isinstance(selected_celltype, type(None)) and Updated:
+        ### NEW CODE
+        tita="155,192 annotated nuclei from four brain regions"
+        #st.write(tit)
+        html_stra = f"""
+            <style>
+            p.a {{
+            font: bold {16}px Courier;
+            text-align: center;
+            }}
+            </style>
+            <p class="a">{tita}</p>
+            """
+        st.markdown(html_stra, unsafe_allow_html=True)
+        
+        cc1,cc2=st.columns([1,1])
+        with cc1:
+            dot10=pg.scatter(st.session_state['adata_annot'],attrs=['new_anno'],basis='umap', wspace=.02,legend_loc='on data',legend_fontsize=7,return_fig=True)
+            dot10.get_figure().gca().set_title("")
+            dot10.get_figure().gca().axis('off')
+            
+            xmin, xmax = dot10.get_figure().gca().get_xaxis().get_view_interval()
+            ymin, ymax = dot10.get_figure().gca().get_yaxis().get_view_interval()
+            dot10.get_figure().gca().arrow(xmin, ymin, xmax/4, 0, head_width=0.2, head_length=0.3, linewidth=.5, color='k', length_includes_head=True)
+            dot10.get_figure().gca().text(x=.1*xmin, y=ymin, s="UMAP1", rotation=0, fontsize=5, color='k')
+            dot10.get_figure().gca().arrow(xmin, ymin, 0,ymax/4, head_width=0.2, head_length=0.3, linewidth=.5, color='k', length_includes_head=True)
+            dot10.get_figure().gca().text(x=1.1*xmin, y=.1*ymin, s="UMAP2", rotation=0, fontsize=5, color='k')
+            st.pyplot(dot10)   
+        with cc2:                    
+            dot11=pg.scatter(st.session_state['adata_annot'],attrs=selected_gene,basis='umap', wspace=.02,legend_loc='on data',legend_fontsize=7,return_fig=True)
+            dot11.get_figure().gca().set_title("")
+            #dot11.get_figure().gca().axis('off')     
+            st.pyplot(dot11)   
+        #Subset Young 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']        
+        #Young/Old  but for cell_type
+        adata_YoungAst = adata_Young[adata_Young.obs['new_anno']==selected_celltype]
+        adata_OldAst = adata_Old[adata_Old.obs['new_anno']==selected_celltype]
+        #tit=selected_gene+": coefficient estimate: 0.24 | BH-FDR p=7.91x$10^{-3}$"
+        tit="Expression of "+selected_gene+" across old and young age groups"
+        #st.write(tit)
+        html_str = f"""
+            <style>
+            p.a {{
+            font: bold {16}px Courier;
+            text-align: center;
+            }}
+            </style>
+            <p class="a">{tit}</p>
+            """
+        st.markdown(html_str, unsafe_allow_html=True)
+        cc1,cc2=st.columns([1,1])
+        with cc1:
+        ########
+            dot12=pg.scatter_groups(st.session_state['adata_annot'], attr=selected_gene, basis='umap', groupby='Age_group', cmap='BuPu', vmin=0, vmax=6,show_full=False,nrows=2,return_fig=True)
+            allaxes = dot12.get_figure().get_axes()
+
+            
+            allaxes[0].set_title("All",fontsize=20)
+            allaxes[0].set_ylabel("old",fontsize=20)
+            allaxes[0].set_xlabel("")
+            allaxes[1].set_title("")
+            allaxes[1].set_ylabel("young",fontsize=20)
+            allaxes[1].set_xlabel("")  
+            #allaxes[2].set_visible(False)
+            #allaxes[3].set_visible(False)          
+            allaxes[2].remove() #.set_visible(False)
+            allaxes[3].remove() #set_visible(False)          
+            
+            ##########            
+
+            ###### SWAP THE PLOTS
+            # pos1 = allaxes[0].get_position()
+            # allaxes[0].set_position(allaxes[1].get_position())
+            # allaxes[1].set_position(pos1)   
+            # pos1 = allaxes[2].get_position()         
+            # allaxes[2].set_position(allaxes[3].get_position())
+            # allaxes[3].set_position(pos1)   
+            #######
+
+            fig_width, fig_height = dot12.get_size_inches()
+            st.pyplot(dot12)   
+        with cc2:
+            adata_Ast = st.session_state['adata_annot'][st.session_state['adata_annot'].obs['new_anno']==selected_celltype]
+            #### New
+            dot13=pg.scatter_groups(adata_Ast, attr=selected_gene, basis='umap', groupby='Age_group', cmap='BuPu', vmin=0, vmax=6,show_full=False,nrows=2,return_fig=True,legend_loc='right margin')
+            allaxes1 = dot13.get_figure().get_axes()
+            allaxes1[0].set_title("")
+            allaxes1[0].set_title(selected_celltype,fontsize=20)
+            allaxes1[0].set_xlabel("")
+            allaxes1[0].set_ylabel("")
+            allaxes1[1].set_title("")
+            allaxes1[1].set_xlabel("")            
+            allaxes1[1].set_ylabel("")  
+            
+            chartBox1 = allaxes1[2].get_position()
+            chartBox2 = allaxes1[3].get_position()
+            
+            
+            allaxes1[2].set_position([chartBox1.x0+.2, chartBox1.y0,chartBox1.width,chartBox1.height]) 
+            allaxes1[3].set_position([chartBox2.x0+.2, chartBox2.y0,chartBox2.width,chartBox2.height]) 
+            
+            #dot13.set_size_inches(fig_width+5, fig_height+5)
+            
+                      
+            ####
+            st.pyplot(dot13)
+#with tab2:
+elif opt_selected == 'Age associations for Multiple genes':
+    #set plot theme
+    blupink = sns.palplot(sns.diverging_palette(h_neg=234,h_pos=342,n=9,s=75,l=30,sep=10,center='light'))
+    sns.set_context("paper", font_scale=1)
+    cmap = sns.diverging_palette(h_neg=234,h_pos=342,n=9,s=75,l=30,sep=7,center='light', as_cmap=True)
+    with st.form(key='multi_genes_form'):
+        c1, c2= st.columns([9.9,.1])
+        with c1:
+            multi_genes = st.multiselect(
+            'Select Genes List',
+            st.session_state['genes_list'])
+        Updated_tab2=st.form_submit_button(label = 'Show GeneSet Results')
+    if not isinstance(multi_genes, type(None)) and Updated_tab2:
+        multi_genes = np.sort(multi_genes)
+
+        #####NEW CODE
+        tit="expression per cell type"
+        #st.write(tit)
+        html_str1 = f"""
+            <style>
+            p.a {{
+            font: bold {16}px Courier;
+            text-align: center;
+            }}
+            </style>
+            <p class="a">{tit}</p>
+            """
+        
+        cc1,cc2=st.columns([1,1])
+        with cc1:
+            try:
+                
+                dot21=pg.dotplot(st.session_state['adata_annot'], genes=multi_genes, groupby='new_anno',switch_axes=True,return_fig=True,cmap='BuPu')
+                dot21.set_figheight(len(multi_genes)*.5)
+                allaxes21 = dot21.get_figure().get_axes()
+                #allaxes21[0].set_title("expression per cell type")
+                allaxes21[0].set_xlabel("")
+
+                st.markdown(html_str1, unsafe_allow_html=True)
+                st.pyplot(dot21)
+            except:
+                st.write("**An exception has occurred, Please check the GeneSet**")
+
+        with cc2:        
+            # now cluster_map
+            celltype_DAGS = st.session_state['cluster_table'][st.session_state['cluster_table'].eval("type.str.endswith('cell_type').values")]
+            celltype_DAGS = celltype_DAGS.pivot(index='feature', columns='tissue')['estimate']
+            #replace NaN with zeroes
+            celltype_DAGS.fillna(0, inplace=True)
+            
+            #query which cell type DAGs are in ad gene set
+            celltype_ad_genes = celltype_DAGS.loc[celltype_DAGS.index.isin(multi_genes)]
+            lst=list(celltype_ad_genes.index)
+            
+            tit="age association per cell type"
+            #st.write(tit)
+            html_str1 = f"""
+                <style>
+                p.a {{
+                font: bold {16}px Courier;
+                text-align: center;
+                }}
+                </style>
+                <p class="a">{tit}</p>
+                """
+
+            st.markdown(html_str1, unsafe_allow_html=True)
+            
+            if celltype_ad_genes.shape[0] > 1:
+                kws = dict(cbar_kws=dict(label='coefficient estimates', orientation='vertical',shrink='.05'))
+                with sns.axes_style({"axes.edgecolor": "black"}):                    
+                    if len(lst)>50:
+                        g = sns.clustermap(celltype_ad_genes, figsize=(6,len(lst)*.2), linewidth=0.05, cmap=cmap, col_cluster=True, dendrogram_ratio=0.1, row_cluster=True, vmin=-1, vmax=1, center= 0, linecolor= 'white', clip_on=False,
+                                            xticklabels=True, yticklabels=True, square=False,cbar_kws={"orientation": "vertical","label": "coefficient estimates"})
+
+                        x0, _y0, _w, _h = g.cbar_pos
+                        g.ax_cbar.set_position([x0+.95, 0.75, .02, len(lst)*.0005])
+                    else:
+                        g = sns.clustermap(celltype_ad_genes, figsize=(6,6), linewidth=0.05, cmap=cmap, col_cluster=True, dendrogram_ratio=0.1, row_cluster=True, vmin=-1, vmax=1, center= 0, linecolor= 'white', clip_on=False,
+                                            xticklabels=True, yticklabels=True, square=False,cbar_kws={"orientation": "vertical","label": "coefficient estimates"})
+
+                        x0, _y0, _w, _h = g.cbar_pos
+                        
+                    g.ax_cbar.set_position([x0+.95, 0.6, .02, .25])
+                    #g.ax_cbar.set_title('coefficient estimates')
+                g.ax_cbar.tick_params(axis='x', length=10)
+                ax = g.ax_heatmap
+                #ax.title("age association per cell type")
+                ax.set_ylabel("")
+                ax.set_xlabel("") 
+                #ax.set_title("age association per cell type")
+                st.pyplot(g)
+                disp_table(celltype_ad_genes)
+                st.dataframe(celltype_ad_genes)
+                
+                #
+            else:
+                st.write('**Got Empty Data Set (from aging.glmmtmb_age_diffs_fdr.csv), Please select a different set of genes**')
+        ######END NEW CODE
+#with tab3:
+elif opt_selected == 'Age associations with GoTerms':
+    #set plot theme
+    blupink = sns.palplot(sns.diverging_palette(h_neg=234,h_pos=342,n=9,s=75,l=30,sep=10,center='light'))
+    sns.set_context("paper", font_scale=1)
+    cmap = sns.diverging_palette(h_neg=234,h_pos=342,n=9,s=75,l=30,sep=7,center='light', as_cmap=True)
+
+    with st.form(key='GoDatabase_form'):
+
+        selected_go_database = st.selectbox(
+        'Please select Pathway Database',st.session_state['go_table'])
+        selected_pway_data=st.session_state['all_pwaydata'][selected_go_database]
+        
+        change = st.form_submit_button("Updata")
+        c1, c2 = st.columns([9.999,.001])
+        with c1:
+            selected_pway_data=st.session_state['all_pwaydata'][selected_go_database]
+            #st.write(selected_go_database)
+            
+            go_term = st.selectbox(
+            'Select GO Term',
+            list(selected_pway_data.columns))
+ 
+        Updated_tab3=st.form_submit_button(label = 'Show Pathway Results')
+
+    #if not isinstance(multi_genes, type(None)) and Updated_tab3:# and Updated_tab4:
+    if not isinstance(go_term, type(None)) and Updated_tab3:# and Updated_tab4:
+
+        multi_genes = [x for x in selected_pway_data[go_term] if str(x) != 'nan']
+        multi_genes = list(set(multi_genes))
+        multi_genes=np.sort(multi_genes)
+        #####NEW CODE
+        cc1,cc2=st.columns([1,1])
+        with cc1:
+
+            st.markdown('**Pathway Database:** '+selected_go_database) #+'\n**Pathway Selected:** '+go_term)
+            st.markdown('**Pathway Selected:** '+go_term)
+            fig = plt.figure(figsize=(1, 1))
+            try:
+                dot21=pg.dotplot(st.session_state['adata_annot'], genes=multi_genes, groupby='new_anno',switch_axes=True,return_fig=True,cmap='BuPu')#, dpi=300.0)
+                dot21.set_figheight(len(multi_genes)*.3)
+                allaxes21 = dot21.get_figure().get_axes()
+                allaxes21[0].set_title("expression per cell type")
+                allaxes21[0].set_xlabel("")
+                st.pyplot(dot21)
+            except:
+                st.write("**An exception has occurred, Please check the GeneSet**")
+
+        with cc2:
+        # now cluster_map
+            celltype_DAGS = st.session_state['cluster_table'][st.session_state['cluster_table'].eval("type.str.endswith('cell_type').values")]
+            celltype_DAGS = celltype_DAGS.pivot(index='feature', columns='tissue')['estimate']
+            #replace NaN with zeroes
+            celltype_DAGS.fillna(0, inplace=True)
+            
+            #query which cell type DAGs are in ad gene set
+            celltype_ad_genes = celltype_DAGS.loc[celltype_DAGS.index.isin(multi_genes)]
+            
+            st.markdown('Please note that **number of Genes in Pathway are:** '+str(len(multi_genes))) #+' \nAnd Genes **in aging.glmmtmb_age_diffs_fdr.csv file are:** '+str(len(celltype_ad_genes))) 
+            st.markdown('Genes **in aging.glmmtmb_age_diffs_fdr.csv file are:** '+str(len(celltype_ad_genes))) 
+            #st.write('**Common List Is:** ')
+            tit="age association per cell type"
+            #st.write(tit)
+            html_str1 = f"""
+                <style>
+                p.a {{
+                font: bold {16}px Courier;
+                text-align: center;
+                }}
+                </style>
+                <p class="a">{tit}</p>
+                """
+
+            
+            
+            lst=list(celltype_ad_genes.index)
+            if celltype_ad_genes.shape[0] > 1:   
+                st.markdown(html_str1, unsafe_allow_html=True)
+                kws = dict(cbar_kws=dict(label='coefficient estimates', orientation='vertical',shrink='.05'))
+                with sns.axes_style({"axes.edgecolor": "black"}):
+                    if len(lst)>50:
+                        g = sns.clustermap(celltype_ad_genes, figsize=(6,len(lst)*.2), linewidth=0.05, cmap=cmap, col_cluster=True, dendrogram_ratio=0.1, row_cluster=True, vmin=-1, vmax=1, center= 0, linecolor= 'white', clip_on=False,
+                                            xticklabels=True, yticklabels=True, square=False,cbar_kws={"orientation": "vertical","label": "coefficient estimates"})
+
+                        x0, _y0, _w, _h = g.cbar_pos
+                            #g.ax_cbar.set_position([x0+.9, 0.3, g.ax_row_dendrogram.get_position().width, 0.5])
+                        g.ax_cbar.set_position([x0+.95, 0.75, .02, len(lst)*.0005])
+                    else:
+                        g = sns.clustermap(celltype_ad_genes, figsize=(6,6), linewidth=0.05, cmap=cmap, col_cluster=True, dendrogram_ratio=0.1, row_cluster=True, vmin=-1, vmax=1, center= 0, linecolor= 'white', clip_on=False,
+                                            xticklabels=True, yticklabels=True, square=False,cbar_kws={"orientation": "vertical","label": "coefficient estimates"})
+
+                        x0, _y0, _w, _h = g.cbar_pos
+                            #g.ax_cbar.set_position([x0+.9, 0.3, g.ax_row_dendrogram.get_position().width, 0.5])
+                        g.ax_cbar.set_position([x0+.95, 0.6, .02, .25])
+                    #g.ax_cbar.set_title('coefficient estimates')
+                g.ax_cbar.tick_params(axis='x', length=10)
+                ax = g.ax_heatmap
+                #ax.title("age association per cell type")
+                ax.set_ylabel("")
+                ax.set_xlabel("") 
+                    #ax.set_title("age association per cell type")
+                #g.fig.suptitle('age association per cell type') 
+                st.pyplot(g)
+                
+                disp_table(celltype_ad_genes)
+                st.dataframe(celltype_ad_genes)
+                
+
+            else:
+                st.write('**Got Empty Data Set (aging.glmmtmb_age_diffs_fdr.csv), Please select a different set of genes**')
+        ######END NEW CODE
+    
+#with readme:
+else:
+    expander = st.expander("How to use this app")   
+    #st.header('How to use this app')
+    expander.markdown('This app consists of 3-Tabs')
+    expander.markdown('**Tab1: Gene Expression by CellType:** Two dropdown lists are provided where user can select a gene and celltype of interest. After making selection, Please press Go Button')
+    expander.markdown('**Tabe2: Age assosciation for Multiple genes: ** A multiselect drop down list is provided to select geens of interest. A dotplot showing expression per cell type for the selected genes is shown on the left. Right plot shows age association per cell type as clustermap for the selected genes.')
+    expander.markdown('**Tab3: Age assosciations with GoTerms: Here user can select a pathway database from a dropdown list of pathway databaass. Once a pathway database is selected (after pressing Update button), another drowdown list shows all pathwys(GoTerms) for the selected database. After selecting a pathway of choice, Please press Show Pathway Results button.')
+    expander.markdown('**README: This tab**')
+    
+    expander1 = st.expander('Introduction')
+    
+    expander1.markdown(
+        """ Coming Soon.
+            """
+            )
+    expander1.markdown('Coming soon')
+    expander1.markdown('- ')
+    expander1.markdown('- ')
+    expander1.markdown('- ')
+    expander1.markdown('Coming soon')
+
+css = '''
+<style>
+    .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
+    font-size:1.5rem;
+    }
+</style>
+'''
+
+st.markdown(css, unsafe_allow_html=True)

megan_pathways/AD GWAS - Sheet1.csv

@@ -0,0 +1,112 @@
+ABCA1
+ABCA7
+ABI3
+ACE
+ADAM10
+ADAM17
+ADAMTS1
+ADAMTS4
+AGRN
+ALPK2
+ANK3
+ANKH
+APH1B
+APOE
+APP
+BCKDK
+BIN1
+BLNK
+CASS4
+CD2AP
+CD33
+CELF1
+CLNK
+CLU
+CNN2
+CNTNAP2
+COX7C
+CR1
+CSTF1
+CTSB
+CTSH
+CYB561
+DOC2A
+ECHDC3
+EED
+EPDR1
+EPHA1
+FAM171A2
+FERMT2
+FOXF1
+GPR141
+GRN
+HAVCR2
+HESX1
+HLA-DQA1
+HLA-DRB1
+HS3ST5
+ICA1
+IDUA
+IL34
+INPP5D
+IQCK
+JAZF1
+KAT8
+KLF16
+LILRA5
+LILRB2
+MAF
+MAMSTR
+MEF2C
+MEIOSIN
+MINDY2
+MME
+MS4A4A
+MS4A6A
+MYO15A
+NCK2
+NECTIN2
+NYAP1
+OARD1
+OPLAH
+PICALM
+PILRA
+PLCG2
+PLEKHA1
+PRDM7
+PRKD3
+PSMC3
+PTK2B
+RASGEF1C
+RBCK1
+RHOH
+SCIMP
+SEC61G
+SHARPIN
+SIGLEC11
+SLC24A4
+SLC2A4RG
+SNX1
+SORL1
+SORT1
+SPDYE3
+SPI1
+SPPL2A
+TMEM106B
+TMEM121
+TNIP1
+TPCN1
+TREM2
+TREML2
+TSPAN14
+TSPOAP1
+UMAD1
+UNC5CL
+USP6NL
+WDR12
+WDR81
+WNT3
+WWOX
+ZCWPW1
+ZFP3
+ZNF652

megan_pathways/GO_Biological_Process_2021.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a9ff0f5c1c41c1f7aa108d79b28e918ee533980ed4a39a389ba97fb6561636e
+size 14874779

megan_pathways/PD GWAS.xlsx - Sheet1.csv

@@ -0,0 +1,87 @@
+ASXL3
+BAG3
+BIN3
+BST1
+C5ORF24
+CAB39L
+CAMK2D
+CASC16
+CD19
+CHD9
+CHRNB1
+CLCN3
+CRHR1
+CRLS1
+CTSB
+DLG2
+DNAH17
+DYRK1A
+ELOVL7
+FAM47E
+FAM47E-STBD1
+FAM49B
+FBRSL1
+FCGR2A
+FGF20
+FYN
+GAK
+GALC
+GBA
+GBF1
+GCH1
+GPNMB
+GRN
+GS1-124K5.11
+HLA-DRB5
+IGSF9B
+INPP5F
+IP6K2
+ITGA8
+ITPKB
+HIP1R
+KCNIP3
+KCNS3
+KPNA1
+KRTCAP2
+L0C100131289
+LCORL
+LINC00693
+LRRK2
+MAP4K4
+MBNL2
+MCCC1
+MED12L
+MED13
+MEX3C
+MIPOL1
+NOD2
+NUCKS1
+PAM
+PMVK
+RAB29
+RETREG3
+RIMS1
+RIT2
+RNF141
+RPS12
+RPS6KL1
+SATB1
+SCAF11
+SCARB2
+SETD1A
+SH3GL2
+SIPA1L2
+SNCA
+SPPL2B
+SPTSSB
+STBD1
+STK39
+SYT17
+TMEM163
+TMEM175
+TRIM40
+UBAP2
+UBTF
+VAMP4
+VPS13C
+WNT3

new_multiregion_brainaging_annotated.h5ad

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7af1f58b193083f71b55215a008db6ad24c1dccb629a9df96e21ad79c16daace
+size 9755245688

requirements.txt

@@ -0,0 +1,12 @@
+streamlit
+pandas
+numpy
+cython
+pegasuspy
+matplotlib
+seaborn
+decoupler
+scipy
+anndata
+typing
+#streamlit-aggrid