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working funcoup demo
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@dcolinmorgan
dcolinmorgan committed Oct 17, 2023
1 parent 7a42bc4 commit d9398f8
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3 changes: 2 additions & 1 deletion src/python/requirements-app.txt
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
###
### Use this file for your custom view requirements here vs in the requirements-system.txt
###
###
umap-learn
128 changes: 87 additions & 41 deletions src/python/views/demo_00_funcoup/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
# Controls how entrypoint.py picks it up


app_id = 'app_05'
app_id = 'app_00'
logger = logging.getLogger(app_id)
urlParams = URLParam(app_id)

Expand Down Expand Up @@ -53,7 +53,7 @@ def custom_css():
# #https://docs.streamlit.io/en/stable/api.html#display-interactive-widgets
def sidebar_area():
st.sidebar.title("Pick an Organism")
option_to_label = {
species_to_label = {
'A.thaliana': 'A.thaliana',
'B.subtilis': 'B.subtilis',
'B.taurus': 'B.taurus',
Expand All @@ -79,8 +79,18 @@ def sidebar_area():

base_url = os.environ['BASE_URL']

filter_by_org_type_init = urlParams.get_field('filter_by_org', default='H.sapiens')
filter_by_org_type = \
st.sidebar.selectbox(
'Choose species:',
('A.thaliana', 'B.subtilis', 'B.taurus','C.elegans','C.familiaris','C.intestinalis','D.discoideum','D.melanogaster','D.rerio','E.coli','G.gallus','H.sapiens','M.jannascii','M.musculus','O.sativa','P.falciparum','R.norvegicus','S.cerevisiae','S,pombe','S.scrofa','S.solfataticus'),
index=('A.thaliana', 'B.subtilis', 'B.taurus','C.elegans','C.familiaris','C.intestinalis','D.discoideum','D.melanogaster','D.rerio','E.coli','G.gallus','H.sapiens','M.jannascii','M.musculus','O.sativa','R.norvegicus','S.cerevisiae','S,pombe','S.scrofa','S.solfataticus').index(filter_by_org_type_init),
format_func=(lambda option: species_to_label[option]))
urlParams.set_field('filter_by_org', filter_by_org_type)


st.sidebar.title("Edge Type")
option_to_label = {
edge_to_label = {
'PFC': 'The confidence Score',
'FBS_max': 'The final Bayesian Score (FBS) of the strongest coupling class',
'FBS_*': 'Bayesian Score of all coupling classes',
Expand All @@ -89,41 +99,56 @@ def sidebar_area():
'Max_category': 'strongest couping class for this pair. This is also the class for which the LLRs are given'
}

filter_by_org_type_init = urlParams.get_field('filter_by_org', default='all')
filter_by_org_type = \
st.sidebar.selectbox(
'Filter nodes by:',
('A.thaliana', 'B.subtilis', 'B.taurus','C.elegans','C.familiaris','C.intestinalis','D.discoideum','D.melanogaster','D.rerio','E.coli','G.gallus','H.sapiens','M.jannascii','M.musculus','O.sativa','P.falciparum','R.norvegicus','S.cerevisiae','S,pombe','S.scrofa','S.solfataticus'),
index=('A.thaliana', 'B.subtilis', 'B.taurus','C.elegans','C.familiaris','C.intestinalis','D.discoideum','D.melanogaster','D.rerio','E.coli','G.gallus','H.sapiens','M.jannascii','M.musculus','O.sativa','R.norvegicus','S.cerevisiae','S,pombe','S.scrofa','S.solfataticus').index(filter_by_org_type_init),
format_func=(lambda option: option_to_label[option]))
urlParams.set_field('filter_by_org', filter_by_org_type)

filter_by_node_type_init = urlParams.get_field('filter_by_node', default='all')
filter_by_node_type_init = urlParams.get_field('filter_by_node', default='PFC')
filter_by_node_type = \
st.sidebar.selectbox(
'Filter nodes by:',
'Filter node by:',
('PFC','FBS_max','FBS_*','LLR_*','LLR_*','Max_category'),
index=('PFC','FBS_max','FBS_*','LLR_*','LLR_*','Max_category').index(filter_by_node_type_init),
format_func=(lambda option: option_to_label[option]))
format_func=(lambda option: edge_to_label[option]))
urlParams.set_field('filter_by_node', filter_by_node_type)

filter_by_net_type_init = urlParams.get_field('filter_by_net', default='all')

st.sidebar.title("Net Type")
net_to_label = {
'compact': 'compact',
'full': 'full',
}

filter_by_net_type_init = urlParams.get_field('filter_by_net', default='compact')
filter_by_net_type = \
st.sidebar.selectbox(
'Filter nodes by:',
'Filter net by:',
('compact','full'),
index=('compact','full').index(filter_by_net_type_init),
format_func=(lambda option: option_to_label[option]))
format_func=(lambda option: net_to_label[option]))
urlParams.set_field('filter_by_net', filter_by_net_type)


st.sidebar.title("Umap Type")
umap_to_label = {
True: True,
False: False,
}

filter_by_umap_type_init = urlParams.get_field('filter_by_umap', default=False)
filter_by_umap_type = \
st.sidebar.selectbox(
'Filter umap by:',
(True,False),
index=(True,False).index(filter_by_umap_type_init),
format_func=(lambda option: umap_to_label[option]))
urlParams.set_field('filter_by_umap', filter_by_umap_type)

# edges_df = pd.read_csv('https://funcoup.org/downloads/download.action?type=network&instanceID=24480085&fileName=FC5.0_B.taurus_compact.gz', sep='\t')
edges_df = pd.read_csv('https://funcoup.org/downloads/download.action?type=network&instanceID=24480085&fileName=FC5.0_'+filter_by_org_type+'_'+filter_by_net_type+'.gz', sep='\t')

return {
# 'n': n,
'edges_df': edges_df,
'organism': filter_by_org_type,
'node_type': filter_by_node_type,
'net_type': filter_by_net_type,
# 'org_type': filter_by_org_type,
# 'node_type': filter_by_node_type,
# 'net_type': filter_by_net_type,
'umap_type': filter_by_umap_type,
}


Expand All @@ -136,10 +161,12 @@ def sidebar_area():

#@st.cache(suppress_st_warning=True, allow_output_mutation=True)
@st.cache_data
def run_filters(node_type, organism, net_type, edges_df):

filtered_edges_df = edges_df['Gene_1','Gene_2',node_type]
def run_filters(edges_df,umap_type=False):

filtered_edges_df = edges_df[:100000] #[['2:Gene1','3:Gene2','1:FBS_max','#0:PFC']]
filtered_edges_df = filtered_edges_df.replace({'ENSG00000':''},regex=True)
# filtered_edges_df.rename(columns={'2:Gene1':'Gene1','3:Gene2':'Gene2','#0:PFC':'PFC','1:FBS_max':'FBS'},inplace=True)
filtered_edges_df.columns=filtered_edges_df.columns.str.split(':').str[1]
# filtered_edges_df = edges_df
# if node_type == 'all':
# pass
Expand All @@ -160,7 +187,7 @@ def run_filters(node_type, organism, net_type, edges_df):
# filtered_edges_df = filtered_edges_df[ filtered_edges_df['d'] <= node_range[1] ]

# include viz generation as part of cache
url = plot_url(filtered_edges_df)
url = plot_url(filtered_edges_df,umap_type)

return {
'edges_df': filtered_edges_df,
Expand All @@ -175,27 +202,46 @@ def run_filters(node_type, organism, net_type, edges_df):
############################################


def plot_url(edges_df):
def plot_url(edges_df,umap_type=False):

# nodes_df = pd.DataFrame({
# 'n': pd.concat([edges_df['s'], edges_df['d']]).unique()
# })
nodes_df = pd.DataFrame({
'n': pd.concat([edges_df['Gene1'], edges_df['Gene2']]).unique()
})
n = len(nodes_df)

# nodes_df['nc'] = nodes_df['n'].apply(lambda v: 0x01000000 * round(255 * v / n))
nodes_df['nc'] = nodes_df['n'].replace('ENSG00000','',regex=True)
nodes_df['nc'] = pd.to_numeric(nodes_df['nc'], errors='coerce')
nodes_df['nc'] = nodes_df['nc'].fillna(0).apply(lambda v: 0x01000000 * round(255 * v / n))

logger.info('Starting graphistry plot')
if not GraphistrySt().test_login():
return ''

url = graphistry\
.bind(source="Gene_1", destination="Gene_2")\
.edges(edges_df)\
.settings(url_params={
'pointSize': 0.3,
'splashAfter': 'false',
'bg': '%23' + 'f0f2f6'
})\
.plot(render=False)

if umap_type == False:
url = graphistry\
.edges(edges_df)\
.bind(source="Gene1", destination="Gene2")\
.nodes(nodes_df)\
.bind(node='n', point_color='nc')\
.settings(url_params={
'pointSize': 0.3,
'splashAfter': 'false',
'bg': '%23' + 'f0f2f6'
})\
.plot(render=False)
elif umap_type == True:
url = graphistry\
.edges(edges_df)\
.bind(source="Gene1", destination="Gene2")\
.nodes(nodes_df)\
.bind(node='n', point_color='nc')\
.settings(url_params={
'pointSize': 0.3,
'splashAfter': 'false',
'bg': '%23' + 'f0f2f6'
})\
.umap(engine='umap_learn')\
.plot(render=False)

logger.info('Generated viz, got back urL: %s', url)

Expand Down

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