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Create pages/corg.py

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1
+ # Dash app to visualize scRNA-seq data quality control metrics from scanpy objects
2
+ # Shoutout to Coding-with-Adam for the initial template of the project:
3
+ # https://github.com/Coding-with-Adam/Dash-by-Plotly/blob/master/Dash%20Components/Graph/dash-graph.py
4
+
5
+ import dash
6
+ from dash import dcc, html, Output, Input
7
+ import plotly.express as px
8
+ import dash_callback_chain
9
+ import yaml
10
+ import polars as pl
11
+ import os
12
+ pl.enable_string_cache(False)
13
+
14
+ dash.register_page(__name__)
15
+
16
+ dataset = "data10xflex/corg/10xflexcorg_umap_clusres"
17
+
18
+ # Set custom resolution for plots:
19
+ config_fig = {
20
+ 'toImageButtonOptions': {
21
+ 'format': 'svg',
22
+ 'filename': 'custom_image',
23
+ 'height': 600,
24
+ 'width': 700,
25
+ 'scale': 1,
26
+ }
27
+ }
28
+ from adlfs import AzureBlobFileSystem
29
+ mountpount=os.environ['AZURE_MOUNT_POINT'],
30
+ AZURE_STORAGE_ACCESS_KEY=os.getenv('AZURE_STORAGE_ACCESS_KEY')
31
+ AZURE_STORAGE_ACCOUNT=os.getenv('AZURE_STORAGE_ACCOUNT')
32
+
33
+ # Load in config file
34
+ config_path = "./data/config.yaml"
35
+
36
+ # Add the read-in data from the yaml file
37
+ def read_config(filename):
38
+ with open(filename, 'r') as yaml_file:
39
+ config = yaml.safe_load(yaml_file)
40
+ return config
41
+
42
+ config = read_config(config_path)
43
+ path_parquet = config.get("path_parquet")
44
+ col_batch = config.get("col_batch")
45
+ col_features = config.get("col_features")
46
+ col_counts = config.get("col_counts")
47
+ col_mt = config.get("col_mt")
48
+
49
+ #filepath = f"az://{path_parquet}"
50
+
51
+ storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY,'anon': False}
52
+ #azfs = AzureBlobFileSystem(**storage_options )
53
+
54
+ # Load in multiple dataframes
55
+ df = pl.read_parquet(f"az://{dataset}.parquet", storage_options=storage_options)
56
+
57
+ # Setup the app
58
+ #external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
59
+ #app = dash.Dash(__name__, use_pages=True) #, requests_pathname_prefix='/dashboard1/'
60
+
61
+ #df = pl.read_parquet(filepath,storage_options=storage_options)
62
+ #df = pl.DataFrame()
63
+ #abfs = AzureBlobFileSystem(account_name=accountname,account_key=accountkey)
64
+ #df = df.rename({"__index_level_0__": "Unnamed: 0"})
65
+
66
+ #df1 = pl.read_parquet(filepath, storage_options=storage_options)
67
+
68
+ #df2 = pl.read_parquet(f"az://data10xflex/{dataset_chosen}.parquet", storage_options=storage_options)
69
+
70
+ #tab0_content = html.Div([
71
+ # html.Label("Dataset chosen"),
72
+ # dcc.Dropdown(id='dpdn1', value="corg/10xflexcorg_umap_clusres", multi=False,
73
+ # options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
74
+ #])
75
+
76
+ #@app.callback(
77
+ # Input(component_id='dpdn1', component_property='value')
78
+ #)
79
+
80
+ #def update_filepath(dpdn1):
81
+ # global df
82
+ # if str(f"az://data10xflex/{dpdn1}.parquet") != str(filepath):
83
+ # print("not identical filepath, chosing other")
84
+ # df2 = pl.read_parquet(f"az://data10xflex/{dpdn1}.parquet", storage_options=storage_options)
85
+ # df = df2
86
+ # return
87
+
88
+ #df = pl.read_parquet(filepath, storage_options=storage_options)
89
+ min_value = df[col_features].min()
90
+ max_value = df[col_features].max()
91
+
92
+ min_value_2 = df[col_counts].min()
93
+ min_value_2 = round(min_value_2)
94
+ max_value_2 = df[col_counts].max()
95
+ max_value_2 = round(max_value_2)
96
+
97
+ min_value_3 = df[col_mt].min()
98
+ min_value_3 = round(min_value_3, 1)
99
+ max_value_3 = df[col_mt].max()
100
+ max_value_3 = round(max_value_3, 1)
101
+
102
+ # Loads in the conditions specified in the yaml file
103
+
104
+ # Note: Future version perhaps all values from a column in the dataframe of the parquet file
105
+ # Note 2: This could also be a tsv of the categories and own specified colors
106
+ #conditions = df[col_batch].unique().to_list()
107
+ # Create the first tab content
108
+ # Add Sliders for three QC params: N genes by counts, total amount of reads and pct MT reads
109
+
110
+ tab1_content = html.Div([
111
+ html.Label("Column chosen"),
112
+ dcc.Dropdown(id='dpdn2', value="batch", multi=False,
113
+ options=df.columns),
114
+ html.Label("N Genes by Counts"),
115
+ dcc.RangeSlider(
116
+ id='range-slider-1',
117
+ step=250,
118
+ value=[min_value, max_value],
119
+ marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
120
+ ),
121
+ dcc.Input(id='min-slider-1', type='number', value=min_value, debounce=True),
122
+ dcc.Input(id='max-slider-1', type='number', value=max_value, debounce=True),
123
+ html.Label("Total Counts"),
124
+ dcc.RangeSlider(
125
+ id='range-slider-2',
126
+ step=7500,
127
+ value=[min_value_2, max_value_2],
128
+ marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
129
+ ),
130
+ dcc.Input(id='min-slider-2', type='number', value=min_value_2, debounce=True),
131
+ dcc.Input(id='max-slider-2', type='number', value=max_value_2, debounce=True),
132
+ html.Label("Percent Mitochondrial Genes"),
133
+ dcc.RangeSlider(
134
+ id='range-slider-3',
135
+ step=5,
136
+ min=0,
137
+ max=100,
138
+ value=[min_value_3, max_value_3],
139
+ ),
140
+ dcc.Input(id='min-slider-3', type='number', value=min_value_3, debounce=True),
141
+ dcc.Input(id='max-slider-3', type='number', value=max_value_3, debounce=True),
142
+ html.Div([
143
+ dcc.Graph(id='pie-graph', figure={}, className='four columns',config=config_fig),
144
+ dcc.Graph(id='my-graph', figure={}, clickData=None, hoverData=None,
145
+ className='four columns',config=config_fig
146
+ ),
147
+ dcc.Graph(id='scatter-plot', figure={}, className='four columns',config=config_fig)
148
+ ]),
149
+ html.Div([
150
+ dcc.Graph(id='scatter-plot-2', figure={}, className='four columns',config=config_fig)
151
+ ]),
152
+ html.Div([
153
+ dcc.Graph(id='scatter-plot-3', figure={}, className='four columns',config=config_fig)
154
+ ]),
155
+ html.Div([
156
+ dcc.Graph(id='scatter-plot-4', figure={}, className='four columns',config=config_fig)
157
+ ]),
158
+ ])
159
+
160
+ # Create the second tab content with scatter-plot-5 and scatter-plot-6
161
+ tab2_content = html.Div([
162
+ html.Div([
163
+ html.Label("S-cycle genes"),
164
+ dcc.Dropdown(id='dpdn3', value="MCM5", multi=False,
165
+ options=[
166
+ "MCM5",
167
+ "PCNA",
168
+ "TYMS",
169
+ "FEN1",
170
+ "MCM2",
171
+ "MCM4",
172
+ "RRM1",
173
+ "UNG",
174
+ "GINS2",
175
+ "MCM6",
176
+ "CDCA7",
177
+ "DTL",
178
+ "PRIM1",
179
+ "UHRF1",
180
+ "MLF1IP",
181
+ "HELLS",
182
+ "RFC2",
183
+ "RPA2",
184
+ "NASP",
185
+ "RAD51AP1",
186
+ "GMNN",
187
+ "WDR76",
188
+ "SLBP",
189
+ "CCNE2",
190
+ "UBR7",
191
+ "POLD3",
192
+ "MSH2",
193
+ "ATAD2",
194
+ "RAD51",
195
+ "RRM2",
196
+ "CDC45",
197
+ "CDC6",
198
+ "EXO1",
199
+ "TIPIN",
200
+ "DSCC1",
201
+ "BLM",
202
+ "CASP8AP2",
203
+ "USP1",
204
+ "CLSPN",
205
+ "POLA1",
206
+ "CHAF1B",
207
+ "BRIP1",
208
+ "E2F8"
209
+ ]),
210
+ html.Label("G2M-cycle genes"),
211
+ dcc.Dropdown(id='dpdn4', value="TOP2A", multi=False,
212
+ options=[
213
+ 'HMGB2', 'CDK1', 'NUSAP1', 'UBE2C', 'BIRC5', 'TPX2', 'TOP2A', 'NDC80', 'CKS2', 'NUF2', 'CKS1B', 'MKI67', 'TMPO', 'CENPF', 'TACC3', 'FAM64A', 'SMC4', 'CCNB2', 'CKAP2L', 'CKAP2', 'AURKB', 'BUB1', 'KIF11', 'ANP32E', 'TUBB4B', 'GTSE1', 'KIF20B', 'HJURP', 'CDCA3', 'HN1', 'CDC20', 'TTK', 'CDC25C', 'KIF2C', 'RANGAP1', 'NCAPD2', 'DLGAP5', 'CDCA2', 'CDCA8', 'ECT2', 'KIF23', 'HMMR', 'AURKA', 'PSRC1', 'ANLN', 'LBR', 'CKAP5',
214
+ 'CENPE', 'CTCF', 'NEK2', 'G2E3', 'GAS2L3', 'CBX5', 'CENPA'
215
+ ]),
216
+ ]),
217
+ html.Div([
218
+ dcc.Graph(id='scatter-plot-5', figure={}, className='three columns',config=config_fig)
219
+ ]),
220
+ html.Div([
221
+ dcc.Graph(id='scatter-plot-6', figure={}, className='three columns',config=config_fig)
222
+ ]),
223
+ html.Div([
224
+ dcc.Graph(id='scatter-plot-7', figure={}, className='three columns',config=config_fig)
225
+ ]),
226
+ html.Div([
227
+ dcc.Graph(id='scatter-plot-8', figure={}, className='three columns',config=config_fig)
228
+ ]),
229
+ ])
230
+
231
+ # Create the second tab content with scatter-plot-5 and scatter-plot-6
232
+ tab3_content = html.Div([
233
+ html.Div([
234
+ html.Label("UMAP condition 1"),
235
+ dcc.Dropdown(id='dpdn5', value="batch", multi=False,
236
+ options=df.columns),
237
+ html.Label("UMAP condition 2"),
238
+ dcc.Dropdown(id='dpdn6', value="n_genes_by_counts", multi=False,
239
+ options=df.columns),
240
+ html.Div([
241
+ dcc.Graph(id='scatter-plot-9', figure={}, className='four columns',config=config_fig)
242
+ ]),
243
+ html.Div([
244
+ dcc.Graph(id='scatter-plot-10', figure={}, className='four columns',config=config_fig)
245
+ ]),
246
+ html.Div([
247
+ dcc.Graph(id='scatter-plot-11', figure={}, className='four columns',config=config_fig)
248
+ ]),
249
+ html.Div([
250
+ dcc.Graph(id='my-graph2', figure={}, clickData=None, hoverData=None,
251
+ className='four columns',config=config_fig
252
+ )
253
+ ]),
254
+ ]),
255
+ ])
256
+ # html.Div([
257
+ # dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
258
+ # ]),
259
+
260
+
261
+ tab4_content = html.Div([
262
+ html.Div([
263
+ html.Label("Multi gene"),
264
+ dcc.Dropdown(id='dpdn7', value=["PAX6","TP63","S100A9"], multi=True,
265
+ options=df.columns),
266
+ ]),
267
+ html.Div([
268
+ dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
269
+ ]),
270
+ ])
271
+
272
+ # Define the tabs layout
273
+ app.layout = html.Div([
274
+ dcc.Tabs(id='tabs', style= {'width': 600,
275
+ 'font-size': '100%',
276
+ 'height': 50}, value='tab1',children=[
277
+ #dcc.Tab(label='Dataset', value='tab0', children=tab0_content),
278
+ dcc.Tab(label='QC', value='tab1', children=tab1_content),
279
+ dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content),
280
+ dcc.Tab(label='Custom', value='tab3', children=tab3_content),
281
+ dcc.Tab(label='Multi dot', value='tab4', children=tab4_content),
282
+ ]),
283
+ ])
284
+
285
+ # Define the circular callback
286
+ @app.callback(
287
+ Output("min-slider-1", "value"),
288
+ Output("max-slider-1", "value"),
289
+ Output("min-slider-2", "value"),
290
+ Output("max-slider-2", "value"),
291
+ Output("min-slider-3", "value"),
292
+ Output("max-slider-3", "value"),
293
+ Input("min-slider-1", "value"),
294
+ Input("max-slider-1", "value"),
295
+ Input("min-slider-2", "value"),
296
+ Input("max-slider-2", "value"),
297
+ Input("min-slider-3", "value"),
298
+ Input("max-slider-3", "value"),
299
+ )
300
+ def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
301
+ return min_1, max_1, min_2, max_2, min_3, max_3
302
+
303
+ @app.callback(
304
+ Output('range-slider-1', 'value'),
305
+ Output('range-slider-2', 'value'),
306
+ Output('range-slider-3', 'value'),
307
+ Input('min-slider-1', 'value'),
308
+ Input('max-slider-1', 'value'),
309
+ Input('min-slider-2', 'value'),
310
+ Input('max-slider-2', 'value'),
311
+ Input('min-slider-3', 'value'),
312
+ Input('max-slider-3', 'value'),
313
+ )
314
+ def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
315
+ return [min_1, max_1], [min_2, max_2], [min_3, max_3]
316
+
317
+ @app.callback(
318
+ Output(component_id='my-graph', component_property='figure'),
319
+ Output(component_id='pie-graph', component_property='figure'),
320
+ Output(component_id='scatter-plot', component_property='figure'),
321
+ Output(component_id='scatter-plot-2', component_property='figure'),
322
+ Output(component_id='scatter-plot-3', component_property='figure'),
323
+ Output(component_id='scatter-plot-4', component_property='figure'), # Add this new scatter plot
324
+ Output(component_id='scatter-plot-5', component_property='figure'),
325
+ Output(component_id='scatter-plot-6', component_property='figure'),
326
+ Output(component_id='scatter-plot-7', component_property='figure'),
327
+ Output(component_id='scatter-plot-8', component_property='figure'),
328
+ Output(component_id='scatter-plot-9', component_property='figure'),
329
+ Output(component_id='scatter-plot-10', component_property='figure'),
330
+ Output(component_id='scatter-plot-11', component_property='figure'),
331
+ Output(component_id='scatter-plot-12', component_property='figure'),
332
+ Output(component_id='my-graph2', component_property='figure'),
333
+ Input(component_id='dpdn2', component_property='value'),
334
+ Input(component_id='dpdn3', component_property='value'),
335
+ Input(component_id='dpdn4', component_property='value'),
336
+ Input(component_id='dpdn5', component_property='value'),
337
+ Input(component_id='dpdn6', component_property='value'),
338
+ Input(component_id='dpdn7', component_property='value'),
339
+ Input(component_id='range-slider-1', component_property='value'),
340
+ Input(component_id='range-slider-2', component_property='value'),
341
+ Input(component_id='range-slider-3', component_property='value')
342
+ )
343
+
344
+ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen,
345
+ batch_chosen = df[col_chosen].unique().to_list()
346
+ dff = df.filter(
347
+ (pl.col(col_chosen).cast(str).is_in(batch_chosen)) &
348
+ (pl.col(col_features) >= range_value_1[0]) &
349
+ (pl.col(col_features) <= range_value_1[1]) &
350
+ (pl.col(col_counts) >= range_value_2[0]) &
351
+ (pl.col(col_counts) <= range_value_2[1]) &
352
+ (pl.col(col_mt) >= range_value_3[0]) &
353
+ (pl.col(col_mt) <= range_value_3[1])
354
+ )
355
+
356
+ #Drop categories that are not in the filtered data
357
+ dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical))
358
+
359
+ dff = dff.sort(col_chosen)
360
+
361
+ # Plot figures
362
+ fig_violin = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
363
+ color=col_chosen, hover_name=col_chosen,template="seaborn")
364
+
365
+ # Cache commonly used subexpressions
366
+ total_count = pl.lit(len(dff))
367
+ category_counts = dff.group_by(col_chosen).agg(pl.col(col_chosen).count().alias("count"))
368
+ category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count"))
369
+
370
+ # Sort the dataframe
371
+ #category_counts = category_counts.sort(col_chosen) does not work check if the names are different ...
372
+
373
+ # Display the result
374
+ total_cells = total_count # Calculate total number of cells
375
+ pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title
376
+
377
+ # Calculate the mean expression
378
+
379
+ # Melt wide format DataFrame into long format
380
+ # Specify batch column as string type and gene columns as float type
381
+ list_conds = condition3_chosen
382
+ list_conds += [col_chosen]
383
+ dff_pre = dff.select(list_conds)
384
+
385
+ # Melt wide format DataFrame into long format
386
+ dff_long = dff_pre.melt(id_vars=col_chosen, variable_name="Gene", value_name="Mean expression")
387
+
388
+ # Calculate the mean expression levels for each gene in each region
389
+ expression_means = dff_long.lazy().group_by([col_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect()
390
+
391
+ # Calculate the percentage total expressed
392
+ dff_long1 = dff_pre.melt(id_vars=col_chosen, variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
393
+ count = 1
394
+ dff_long2 = dff_long1.with_columns(pl.lit(count).alias("len"))
395
+ dff_long3 = dff_long2.filter(pl.col("value") > 0).group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("len"))
396
+ dff_long4 = dff_long2.group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("total"))
397
+ dff_5 = dff_long4.join(dff_long3, on=[col_chosen,"Gene"], how="outer")
398
+ result = dff_5.select([
399
+ pl.when((pl.col('len').is_not_null()) & (pl.col('total').is_not_null()))
400
+ .then(pl.col('len') / pl.col('total')*100)
401
+ .otherwise(None).alias("%"),
402
+ ])
403
+ result = result.with_columns(pl.col("%").fill_null(100))
404
+ dff_5[["percentage"]] = result[["%"]]
405
+ dff_5 = dff_5.select(pl.col(col_chosen,"Gene","percentage"))
406
+
407
+ # Final part to join the percentage expressed and mean expression levels
408
+ # TO DO
409
+ expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
410
+
411
+ # Order the dataframe on ascending categories
412
+ expression_means = expression_means.sort(col_chosen, descending=True)
413
+
414
+ #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
415
+ category_counts = category_counts.sort(col_chosen)
416
+
417
+ fig_pie = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn")
418
+
419
+ #labels = category_counts[col_chosen].to_list()
420
+ #values = category_counts["normalized_count"].to_list()
421
+
422
+ # Create the scatter plots
423
+ fig_scatter = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_chosen,
424
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
425
+ hover_name='batch',template="seaborn")
426
+
427
+ fig_scatter_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt,
428
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
429
+ hover_name='batch',template="seaborn")
430
+
431
+ fig_scatter_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features,
432
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
433
+ hover_name='batch',template="seaborn")
434
+
435
+
436
+ fig_scatter_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts,
437
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
438
+ hover_name='batch',template="seaborn")
439
+
440
+ fig_scatter_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
441
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
442
+ hover_name='batch', title="S-cycle gene:",template="seaborn")
443
+
444
+ fig_scatter_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
445
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
446
+ hover_name='batch', title="G2M-cycle gene:",template="seaborn")
447
+
448
+ fig_scatter_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
449
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
450
+ hover_name='batch', title="S score:",template="seaborn")
451
+
452
+ fig_scatter_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
453
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
454
+ hover_name='batch', title="G2M score:",template="seaborn")
455
+
456
+ # Sort values of custom in-between
457
+ dff = dff.sort(condition1_chosen)
458
+
459
+ fig_scatter_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
460
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
461
+ hover_name='batch',template="seaborn")
462
+
463
+ fig_scatter_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
464
+ labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
465
+ hover_name='batch',template="seaborn")
466
+
467
+ fig_scatter_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
468
+ #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
469
+ hover_name='batch',template="seaborn")
470
+
471
+ fig_scatter_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
472
+ size="percentage", size_max = 20,
473
+ #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
474
+ hover_name=col_chosen,template="seaborn")
475
+
476
+ fig_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
477
+ color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
478
+
479
+
480
+ return fig_violin, fig_pie, fig_scatter, fig_scatter_2, fig_scatter_3, fig_scatter_4, fig_scatter_5, fig_scatter_6, fig_scatter_7, fig_scatter_8, fig_scatter_9, fig_scatter_10, fig_scatter_11, fig_scatter_12, fig_violin2
481
+
482
+ # Set http://localhost:5000/ in web browser
483
+ # Now create your regular FASTAPI application
484
+
485
+ #if __name__ == '__main__':
486
+ # app.run_server(debug=False, use_reloader=False, host='0.0.0.0', port=5000) #