def update_dr(color, alpha):
fig_sc = st.plot_stream_sc(adata, root='S1', color=[color], alpha=alpha,
dist_scale=0.3, show_graph=True, show_text=True, plotly=True, return_fig=True)
fig_sc.update_layout(
autosize=False,
width=450,
height=350,
margin=dict(
l=30,
r=30,
b=5,
t=5,
pad=4
),
plot_bgcolor='rgba(0,0,0,0)'
)
fig_st = st.plot_stream(adata, root='S1', color=[color], return_svg=True)
fig_dr = st.plot_dimension_reduction(adata, color=[color], n_components=3, alpha=alpha, show_graph=True,
show_text=False, plotly=True, return_fig=True)
fig_dr.update_layout(
autosize=False,
width=450,
height=350,
margin=dict(
l=30,
r=30,
b=5,
t=5,
pad=4
),
plot_bgcolor='rgba(0,0,0,0)'
)
return html.Div([
dbc.Row([dcc.Graph(figure=fig_sc, style={'margin-left': 'auto', 'margin-right': 'auto',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'}),
html.Img(src="data:image/svg+xml;base64,{}".format(fig_st),
style={'margin-left': 'auto', 'margin-right': 'auto', 'width': '350px%', 'height': '350px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'})]),
html.Br(),
dbc.Row([dcc.Graph(figure=fig_dr, style={'margin-left': 'auto', 'margin-right': 'auto',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'})
])
])
def tab_content(active_tab):
if active_tab == "tab-qc":
return html.Div(id='qc-graphic'), card_qc
elif active_tab == "tab-dr":
return html.Div(id="dr-graphic"), card_dr
elif active_tab == "tab-sp":
return html.Div(id="st-graphic"), card_sp
else:
fig = st.plot_stream(adata, root='S1', return_svg=True)
return html.Img(
src="data:image/svg+xml;base64,{}".format(fig)), card_ds
def tab_content(active_tab):
if active_tab == "tab-qc":
fig_qc = st.plot_qc(adata,
jitter=0.2,
fig_size=(2, 2),
return_svg=True)
return html.Img(
src="data:image/svg+xml;base64,{}".format(fig_qc)), card_qc
elif active_tab == "tab-dr":
return html.Div(id="dr-graphic"), card_dr
elif active_tab == "tab-sp":
return html.Div(id="st-graphic"), card_sp
else:
fig = st.plot_stream(adata, root='S1', return_svg=True)
return card_content_ds, card_ds
def update_dr(colors, alpha):
fig5 = st.plot_stream_sc(adata,
root='S1',
color=[colors],
alpha=alpha,
dist_scale=0.3,
show_graph=True,
show_text=True,
plotly=True,
return_fig=True)
fig6 = st.plot_stream(adata, root='S1', color=[colors], return_svg=True)
return html.Div([
dbc.Row([
dbc.Col(dcc.Graph(figure=fig5), align="center"),
dbc.Col(html.Img(src="data:image/svg+xml;base64,{}".format(fig6)),
align="center")
])
])
'label': 'Sample Datasets',
'value': 'sample'
}, {
'label': 'Personal Datasets',
'value': 'personal'
}],
inputStyle={
"margin-right": "20px",
"margin-left": "20px"
},
value='sample',
labelStyle={'display': 'inline-block'}),
html.Div(id='data-selection')
])
fig_ds = st.plot_stream(adata, root='S1', return_svg=True)
fig_ds = html.Img(src="data:image/svg+xml;base64,{}".format(fig_ds))
card_content_ds_1 = dbc.FormGroup(
[dbc.Row([dbc.Col(fig_ds)]),
html.P(adata.uns['discription'])])
card_content_ds = dbc.Card(
[
dbc.CardImg(fig_ds, top=True),
dbc.CardBody([
html.H4("Description", className="card-title"),
html.P(
"Some quick example text to build on the card title and "
"make up the bulk of the card's content.",
className="card-text",
def stream_test_Nestorowa_2016():
workdir = os.path.join(_root, 'datasets/Nestorowa_2016/')
temp_folder = tempfile.gettempdir()
tar = tarfile.open(workdir + 'output/stream_result.tar.gz')
tar.extractall(path=temp_folder)
tar.close()
ref_temp_folder = os.path.join(temp_folder, 'stream_result')
print(workdir + 'data_Nestorowa.tsv.gz')
input_file = os.path.join(workdir, 'data_Nestorowa.tsv.gz')
label_file = os.path.join(workdir, 'cell_label.tsv.gz')
label_color_file = os.path.join(workdir, 'cell_label_color.tsv.gz')
comp_temp_folder = os.path.join(temp_folder, 'stream_result_comp')
try:
st.set_figure_params(dpi=80,
style='white',
figsize=[5.4, 4.8],
rc={'image.cmap': 'viridis'})
adata = st.read(file_name=input_file, workdir=comp_temp_folder)
adata.var_names_make_unique()
adata.obs_names_make_unique()
st.add_cell_labels(adata, file_name=label_file)
st.add_cell_colors(adata, file_name=label_color_file)
st.cal_qc(adata, assay='rna')
st.filter_features(adata, min_n_cells=5)
st.select_variable_genes(adata, n_genes=2000, save_fig=True)
st.select_top_principal_components(adata,
feature='var_genes',
first_pc=True,
n_pc=30,
save_fig=True)
st.dimension_reduction(adata,
method='se',
feature='top_pcs',
n_neighbors=100,
n_components=4,
n_jobs=2)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=3,
show_graph=False,
show_text=False,
save_fig=True,
fig_name='dimension_reduction.pdf')
st.plot_visualization_2D(adata,
method='umap',
n_neighbors=100,
color=['label', 'Gata1', 'n_genes'],
use_precomputed=False,
save_fig=True,
fig_name='visualization_2D.pdf')
st.seed_elastic_principal_graph(adata, n_clusters=20)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=2,
show_graph=True,
show_text=False,
save_fig=True,
fig_name='dr_seed.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_seed.pdf')
st.elastic_principal_graph(adata,
epg_alpha=0.01,
epg_mu=0.05,
epg_lambda=0.01)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=2,
show_graph=True,
show_text=False,
save_fig=True,
fig_name='dr_epg.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_epg.pdf')
###Extend leaf branch to reach further cells
st.extend_elastic_principal_graph(adata,
epg_ext_mode='QuantDists',
epg_ext_par=0.8)
st.plot_dimension_reduction(adata,
color=['label'],
n_components=2,
show_graph=True,
show_text=True,
save_fig=True,
fig_name='dr_extend.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_extend.pdf')
st.plot_visualization_2D(
adata,
method='umap',
n_neighbors=100,
color=['label', 'branch_id_alias', 'S4_pseudotime'],
use_precomputed=False,
save_fig=True,
fig_name='visualization_2D_2.pdf')
st.plot_flat_tree(adata,
color=['label', 'branch_id_alias', 'S4_pseudotime'],
dist_scale=0.5,
show_graph=True,
show_text=True,
save_fig=True)
st.plot_stream_sc(adata,
root='S4',
color=['label', 'Gata1'],
dist_scale=0.5,
show_graph=True,
show_text=False,
save_fig=True)
st.plot_stream(adata,
root='S4',
color=['label', 'Gata1'],
save_fig=True)
st.detect_leaf_markers(adata,
marker_list=adata.uns['var_genes'][:300],
root='S4',
n_jobs=4)
st.detect_transition_markers(adata,
root='S4',
marker_list=adata.uns['var_genes'][:300],
n_jobs=4)
st.detect_de_markers(adata,
marker_list=adata.uns['var_genes'][:300],
root='S4',
n_jobs=4)
# st.write(adata,file_name='stream_result.pkl')
except:
print("STREAM analysis failed!")
raise
else:
print("STREAM analysis finished!")
print(ref_temp_folder)
print(comp_temp_folder)
pathlist = Path(ref_temp_folder)
for path in pathlist.glob('**/*'):
if path.is_file() and (not path.name.startswith('.')):
file = os.path.relpath(str(path), ref_temp_folder)
print(file)
if (file.endswith('pdf')):
if (os.path.getsize(os.path.join(comp_temp_folder, file)) > 0):
print('The file %s passed' % file)
else:
raise Exception('Error! The file %s is not matched' % file)
else:
checklist = list()
df_ref = pd.read_csv(os.path.join(ref_temp_folder, file),
sep='\t')
# print(df_ref.shape)
# print(df_ref.head())
df_comp = pd.read_csv(os.path.join(comp_temp_folder, file),
sep='\t')
# print(df_comp.shape)
# print(df_comp.head())
for c in df_ref.columns:
# print(c)
if (is_numeric_dtype(df_ref[c])):
checklist.append(all(np.isclose(df_ref[c],
df_comp[c])))
else:
checklist.append(all(df_ref[c] == df_comp[c]))
if (all(checklist)):
print('The file %s passed' % file)
else:
raise Exception('Error! The file %s is not matched' % file)
print('Successful!')
rmtree(comp_temp_folder, ignore_errors=True)
rmtree(ref_temp_folder, ignore_errors=True)
import matplotlib
matplotlib.use('Agg')
from app import app
### preset data
adata_computed = st.read(
file_name='./SampleData/Nestorowa_2016/Nestorowa-2016.pkl',
workdir='./stream_result')
adata = st.read(
file_name='./SampleData/Nestorowa_2016/Nestorowa-2016-raw.h5ad',
workdir='./stream_result')
adata.uns[
'discription'] = 'This scRNA-seq dataset contains 1656 cells and 40594 genes from mouse hematopoietic stem and progenitor cell differentiation. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20-31 (2016).'
fig_ds = st.plot_stream(adata_computed, root='S1', return_svg=True)
### Set optionals
available_samples = [
'Nestorowa, S. et al. 2016', 'Harrison, S. et al. 2021',
'Trapnell, C. et al. 2014', ' Tang, Q. et al. 2017'
]
available_normalization = [
'Library size correction', 'TF-IDF transformation', 'None'
]
available_DR = [
'Spectral embedding(SE)', 'Modified locally linear embedding(MLLE)',
'UMAP', 'PCA'
]
###----- Header for data selection -----###
def update_dr(gene_source, branch, high, gene):
if high is None:
high = str(eval(branch)[0])
if gene is None:
gene = avaliable_features[0]
if gene_source == "transition":
data_df = adata.uns['transition_markers'][eval(branch)].round(2)
elif gene_source == "leaf":
data_df = adata.uns['leaf_markers'][eval(branch)].astype(float).round(2)
elif gene_source == "diverging":
if high == str(eval(branch)[0]):
data_df = adata.uns['de_markers_greater'][eval(branch)].round(2)
else:
data_df = adata.uns['de_markers_less'][eval(branch)].round(2)
data_df = data_df.reset_index()
data_df.rename(columns={'index': 'gene'}, inplace=True)
fig = go.Figure(data=[go.Table(
header=dict(
values=list(data_df.columns),
line_color='darkslategray', fill_color='lightgrey',
align='center', font=dict(color='black', size=12)
),
cells=dict(values=data_df.T.values,
line_color='darkslategray', align='center', font=dict(color='black', size=11)
))
])
fig_sc = st.plot_stream_sc(adata, root='S1', color=[gene],
dist_scale=0.3, show_graph=True, show_text=True, plotly=True, return_fig=True)
fig_sc.update_layout(
autosize=False,
width=450,
height=350,
margin=dict(
l=30,
r=30,
b=5,
t=5,
pad=4
),
plot_bgcolor='rgba(0,0,0,0)'
)
fig_st = st.plot_stream(adata, root='S1', color=[gene], return_svg=True)
return html.Div([
dbc.Row([dcc.Graph(figure=fig,
style={"maxHeight": "350px", 'width': '100%', "overflow": "scroll", 'margin-left': 'auto',
'margin-right': 'auto'})]),
html.Br(),
dbc.Row([
dcc.Graph(figure=fig_sc, style={'margin-left': 'auto', 'margin-right': 'auto',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'}),
html.Img(src="data:image/svg+xml;base64,{}".format(fig_st),
style={'margin-left': 'auto', 'margin-right': 'auto', 'width': '350px%', 'height': '350px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px'})]),
])
@app.callback(
Output("card_C_ti", "children"),
Output("C4-spinners", "children"),
Input("Compute4-button", "n_clicks")
)
def loading_output(n):
if n:
time.sleep(10)
return card_C_ti_finished, 'Finished'
return "This step is not ready yet. Click the COMPUTE button above and waite it to finish.", "Compute Step 4"
### Fill in contents for each card body
# DS
fig_ds = st.plot_stream(adata_computed, root='S1', return_svg=True)
card_assay = dbc.Card(
[
dbc.CardImg(src="/assets/Data_selection/Assays.png", top=True,
style={'width': '110px', 'margin-left': 'auto', 'margin-right': 'auto', 'marginTop': 5}),
dbc.CardBody(
html.H5("RNA", className="card-text", style={"text-align": 'center'})
),
], color="dark", outline=True
)
card_cell = dbc.Card(
[
dbc.CardImg(src="/assets/Data_selection/NumCells.png", top=True,
style={'width': '110px', 'margin-left': 'auto', 'margin-right': 'auto', 'marginTop': 5}),
