app.py

#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)