Disable button if there's ongoing computation in the analysis pages

callbacks/coexpression/callbacks.py

@@ -1,4 +1,4 @@
-from dash import Input, Output, State, html, dcc
+from dash import Input, Output, State, html, dcc, ctx
 from dash.exceptions import PreventUpdate
 from collections import namedtuple
 
@@ -90,6 +90,17 @@ def display_coexpression_output(coexpression_is_submitted):
 
         else:
             return {'display': 'none'}
+
+    @app.callback(
+        Output('coexpression-submit', 'disabled'),
+
+        Input('coexpression-submit', 'n_clicks'),
+        Input('coexpression-module-graph', 'elements'),
+        Input('coexpression-pathways', 'data'),
+        Input('coexpression-module-stats', 'children')
+    )
+    def disable_coexpression_button_upon_run(n_clicks,  *_):
+        return ctx.triggered_id == 'coexpression-submit' and n_clicks > 0
 
     @app.callback(
         Output('coexpression-parameter-slider', 'marks'),

callbacks/lift_over/callbacks.py

@@ -1,4 +1,4 @@
-from dash import Input, Output, State, dcc, html
+from dash import Input, Output, State, dcc, html, ctx
 from dash.exceptions import PreventUpdate
 
 from .util import *
@@ -53,6 +53,16 @@ def display_lift_over_output(lift_over_is_submitted):
         else:
             return {'display': 'none'}
 
+    @app.callback(
+        Output('lift-over-submit', 'disabled'),
+
+        Input('lift-over-submit', 'n_clicks'),
+        Input('lift-over-results-table', 'data'),
+        Input('lift-over-results-statistics', 'children')
+    )
+    def disable_lift_over_button_upon_run(n_clicks,  *_):
+        return ctx.triggered_id == 'lift-over-submit' and n_clicks > 0
+
     @app.callback(
         Output('lift-over-results-intro', 'children'),
         Output('lift-over-results-tabs', 'children'),

callbacks/lift_over/util.py

@@ -8,6 +8,8 @@
 from ..general_util import *
 from ..links_util import *
 
+import regex as re
+
 
 Genomic_interval = namedtuple('Genomic_interval', ['chrom', 'start', 'stop'])
 
@@ -122,6 +124,12 @@ def to_genomic_interval(genomic_interval_str):
         if is_one_digit_chromosome(chrom):
             chrom = pad_one_digit_chromosome(chrom)
 
+        # Change 'chr' to 'Chr'
+        chrom = re.sub(r'chr', 'Chr', chrom, flags=re.IGNORECASE)
+
+        if not chrom.startswith('Chr') or not chrom[3].isdigit():
+            return errors['NO_CHROM_INTERVAL_SEP'].code, genomic_interval_str
+
     except ValueError:
         return errors['NO_CHROM_INTERVAL_SEP'].code, genomic_interval_str
 

callbacks/text_mining/callbacks.py

@@ -1,4 +1,4 @@
-from dash import Input, Output, State, ctx, ALL, html, no_update
+from dash import Input, Output, State, ctx, ALL, html, no_update, dcc
 from dash.exceptions import PreventUpdate
 
 from .util import *
@@ -92,7 +92,7 @@ def display_coexpression_output(text_mining_is_submitted):
         State('text-mining-query', 'value'),
         Input('text-mining-result-table', 'data'),
     )
-    def trigger(n_clicks, text_mining_query, *_):
+    def disable_text_mining_button_upon_run(n_clicks, text_mining_query, *_):
         is_there_error, _ = is_error(text_mining_query)
 
         if is_there_error:
@@ -144,3 +144,15 @@ def display_text_mining_results(text_mining_is_submitted, homepage_submitted, te
     )
     def reset_table_filters(*_):
         return ''
+
+    @app.callback(
+        Output('text-mining-download-df-to-csv', 'data'),
+        Input('text-mining-export-table', 'n_clicks'),
+        State('text-mining-result-table', 'data'),
+    )
+    def download_text_mining_table_to_csv(download_n_clicks, text_mining_df, ):
+        if download_n_clicks >= 1:
+            df = pd.DataFrame(text_mining_df)
+            return dcc.send_data_frame(df.to_csv, f'Text Mining Analysis Table.csv', index=False)
+
+        raise PreventUpdate

pages/analysis/co_expr.py

@@ -4,73 +4,90 @@
 from callbacks.constants import Constants
 from callbacks.coexpression.util import *
 
+# ============================
+# Module Detection Algorithms
+# ============================
 
-coach = html.Li(
-    [html.B('COACH'),
-     html.Span(
+
+coach = html.Li([
+    html.B('COACH'),
+    html.Span(
         ' Detects highly connected gene subnetworks (referred to as "cores") and expands them by including closely associated genes',
         className='algo-desc'),
-     html.Div([
-         html.Span(
+    html.Div([
+        html.Span(
              'Wu, M., Li, X., Kwoh, C. K., & Ng, S. K. (2009). A core-attachment based method to detect protein complexes in PPI networks. '),
-         html.I('BMC Bioinformatics, 10'),
-         html.Span('(169). '),
-         html.A('https://doi.org/10.1186/1471-2105-10-169',
-                href='https://doi.org/10.1186/1471-2105-10-169',
-                target='_blank')],
-        className='reference'
-    )]
-)
+        html.I('BMC Bioinformatics, 10'),
+        html.Span('(169). '),
+        html.A(
+            'https://doi.org/10.1186/1471-2105-10-169',
+            href='https://doi.org/10.1186/1471-2105-10-169',
+            target='_blank')
+    ], className='reference')
+])
 
-demon = html.Li(
-    [html.B('DEMON'),
-     html.Span(
+demon = html.Li([
+    html.B('DEMON'),
+    html.Span(
         ' Adopts a bottom-up approach where genes "vote" to determine the subnetwork to which connected genes belong',
         className='algo-desc'),
-     html.Div([
-         html.Span(
+    html.Div([
+        html.Span(
              'Coscia, M., Rossetti, G., Giannotti, F., & Pedreschi, D. (2012). DEMON: A local-first discovery method for overlapping communities. In '),
-         html.I('KDD\'12: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining '),
-         html.Span('(pp. 615–623). Association for Computing Machinery. '),
-         html.A('https://doi.org/10.1145/2339530.2339630',
-                href='https://doi.org/10.1145/2339530.2339630',
-                target='_blank')],
-        className='reference'
-    )]
-)
+        html.I('KDD\'12: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining '),
+        html.Span('(pp. 615–623). Association for Computing Machinery. '),
+        html.A(
+            'https://doi.org/10.1145/2339530.2339630',
+            href='https://doi.org/10.1145/2339530.2339630',
+            target='_blank')
+    ], className='reference')
+])
 
-clusterone = html.Li(
-    [html.B('ClusterONE'),
-     html.Span(
+clusterone = html.Li([
+    html.B('ClusterONE'),
+    html.Span(
         ' Forms cohesive gene subnetworks from an initial set of seed genes. ',
         className='algo-desc'),
-     html.Div([
-         html.Span(
+    html.Div([
+        html.Span(
              'Nepusz, T., Yu, H., & Paccanaro, A. (2012). Detecting overlapping protein complexes in protein-protein interaction networks. '),
-         html.I('Nature Methods, 9, '),
-         html.Span('471–472. '),
-         html.A('https://doi.org/10.1038/nmeth.1938',
-                href='https://doi.org/10.1038/nmeth.1938',
-                target='_blank')],
-        className='reference'
-    )],
-)
+        html.I('Nature Methods, 9, '),
+        html.Span('471–472. '),
+        html.A(
+            'https://doi.org/10.1038/nmeth.1938',
+            href='https://doi.org/10.1038/nmeth.1938',
+            target='_blank')
+    ], className='reference')
+])
 
-fox = html.Li([html.B('FOX'),
-               html.Span(
-    ' Determines the membership of a gene to a subnetwork by counting the number of triangles formed by the gene with other genes in the subnetwork',
-    className='algo-desc'),
+fox = html.Li([
+    html.B('FOX'),
+    html.Span(
+        ' Determines the membership of a gene to a subnetwork by counting the number of triangles formed by the gene with other genes in the subnetwork',
+        className='algo-desc'),
     html.Div([
         html.Span(
-             'Lyu, T., Bing, L., Zhang, Z., & Zhang, Y. (2020). FOX: Fast overlapping community detection algorithm in big weighted networks. '),
+            'Lyu, T., Bing, L., Zhang, Z., & Zhang, Y. (2020). FOX: Fast overlapping community detection algorithm in big weighted networks. '),
         html.I('ACM Transactions on Social Computing, 3'),
         html.Span('(3), 1–23. '),
-        html.A('https://doi.org/10.1145/3404970',
-               href='https://doi.org/10.1145/3404970',
-               target='_blank')],
-             className='reference mb-3',
-             ),
-    html.Span('RicePilaf uses LazyFox for a parallelized implementation of FOX')
+        html.A(
+            'https://doi.org/10.1145/3404970',
+            href='https://doi.org/10.1145/3404970',
+            target='_blank')
+    ], className='reference mb-3'),
+    html.Span(
+        'RicePilaf uses LazyFox for a parallelized implementation of FOX. LazyFox is run with the queue size and thread count set to 20.',
+        className='algo-desc'),
+    html.Div([
+        html.Span(
+            'Garrels, T., Khodabakhsh, A., Renard, B. Y., & Baum, K. (2023). LazyFox: Fast and parallelized overlapping community detection in large graphs. '),
+        html.I('PeerJ Computer Science, 9, '),
+        html.Span('e1291. '),
+        html.A(
+            'https://doi.org/10.7717/peerj-cs.1291',
+            href='https://doi.org/10.7717/peerj-cs.1291',
+            target='_blank')
+    ], className='reference mb-3')
 ])
 
 module_detection_algo_modal = dbc.Modal([
@@ -86,13 +103,18 @@
     ])],
     id='coexpression-clustering-algo-modal',
     is_open=False,
-    size='xl'
+    size='xl',
+    scrollable=True
 )
 
+# ======================
+# Coexpression Networks
+# ======================
+
 
 coexpression_network_modal = dbc.Modal([
     dbc.ModalHeader(
-        dbc.ModalTitle('Module Detection Algorithms')
+        dbc.ModalTitle('Coexpression Networks')
     ),
     dbc.ModalBody([
         html.P(
@@ -123,19 +145,19 @@
         html.Div([
             html.P(
                 [
-                   'In this page, you can search for modules (a.k.a. communities, clusters) in rice co-expression networks, '
-                   'which are significantly enriched in the genes implicated by your GWAS. '
-                   'Likely functions of the modules are inferred by enrichment analysis against several ontologies and pathway databases. Click ',
-                   dcc.Link(
-                    ['here ', html.I(
-                        id='demo-link',
-                        className='fa-solid fa-up-right-from-square fa-2xs'
-                    )],
-                    href='https://github.com/bioinfodlsu/rice-pilaf/wiki/2.3-Co%E2%80%90expression-Network-Analysis',
-                    target='_blank',
-                    className='top-navbar-item'
-                ),
-                ' for user guide.'
+                    'In this page, you can search for modules (a.k.a. communities, clusters) in rice co-expression networks, '
+                    'which are significantly enriched in the genes implicated by your GWAS. '
+                    'Likely functions of the modules are inferred by enrichment analysis against several ontologies and pathway databases. Click ',
+                    dcc.Link(
+                        ['here ', html.I(
+                            id='demo-link',
+                            className='fa-solid fa-up-right-from-square fa-2xs'
+                        )],
+                        href='https://github.com/bioinfodlsu/rice-pilaf/wiki/2.3-Co%E2%80%90expression-Network-Analysis',
+                        target='_blank',
+                        className='top-navbar-item'
+                    ),
+                    ' for user guide.'
 
                 ]
             )

pages/homepage.py

@@ -20,16 +20,14 @@
         html.Span('Enter genomic intervals like so: '), html.Span(
             'Chr01:100000-200000', className='text-muted'),
         html.Br(),
-        html.Span(
-            'Multiple intervals should be separated by a semicolon like so: '),
-        html.Span('Chr01:100000-200000;Chr02:300000-400000',
-                  className='text-muted'),
-        html.Br(),
-        html.Span(
+        html.P([
+            html.Span(
+                'Multiple intervals should be separated by a semicolon like so: '),
+            html.Span('Chr01:100000-200000;Chr02:300000-400000',
+                      className='text-muted')
+        ]),
+        html.P(
             'These intervals are obtained from LD-based clumping of significant GWAS SNPs or from QTL mapping studies.'),
-        html.Br(),
-        html.Br(),
-
         html.P(
             'We also provide some sample genomic intervals, taken from the following GWAS/QTL analyses:'),
         html.Ul([
@@ -65,7 +63,8 @@
     ])],
     id='genomic-interval-modal',
     is_open=False,
-    size='xl'
+    size='xl',
+    scrollable=True
 )
 
 # ======