--- a
+++ b/src/dash/apps/clustergrammer.py
@@ -0,0 +1,190 @@
+
+import dash
+import dash_table
+import dash_bootstrap_components as dbc
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output
+import datetime
+import pandas as pd
+
+from data import get_omics_data, get_biomolecule_names, get_combined_data, get_p_values, get_volcano_data
+from plot import volcano_plot
+from nav import navbar
+
+# importing app through index page
+from app import app
+
+print()
+print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+print("Loading data for heatmap...")
+print()
+
+# load metabolomics data matrix
+print("Loading metabolomics data...")
+from app import metabolomics_df, metabolomics_quant_range
+print("Metabolomics data shape: {}".format(metabolomics_df.shape))
+print("Loading lipidomics data...")
+from app import lipidomics_df, lipidomics_quant_range
+print("Lipidomics data shape: {}".format(lipidomics_df.shape))
+print("Loading proteomics data...")
+from app import proteomics_df, proteomics_quant_range
+print("Proteomics data shape: {}".format(proteomics_df.shape))
+print("Loading transcriptomics data...")
+from app import transcriptomics_df, transcriptomics_quant_range
+print("Transcriptomics data shape: {}".format(transcriptomics_df.shape))
+
+available_datasets = ['Proteins', 'Lipids', 'Metabolites', 'Transcripts']
+
+# define dataset dictionaries
+# define dataset dictionaries
+from app import dataset_dict, df_dict, quant_value_range_dict
+from app import metabolomics_biomolecule_names_dict
+from app import lipidomics_biomolecule_names_dict
+from app import proteomics_biomolecule_names_dict
+from app import transcriptomics_biomolecule_names_dict
+
+global_names_dict = {
+    "proteomics":proteomics_biomolecule_names_dict,
+    "lipidomics":lipidomics_biomolecule_names_dict,
+    "metabolomics":metabolomics_biomolecule_names_dict,
+    "transcriptomics":transcriptomics_biomolecule_names_dict,
+    "combined":{**proteomics_biomolecule_names_dict,
+                **lipidomics_biomolecule_names_dict,
+                **metabolomics_biomolecule_names_dict,
+                **transcriptomics_biomolecule_names_dict}
+}
+
+
+
+control_panel = dbc.Card(
+    [
+        dbc.CardHeader("CONTROL PANEL",
+                            style={"background-color":"#5bc0de",
+                                        "font-weight":"bold",
+                                        "font-size":"large"}),
+        dbc.CardBody(
+            [html.P("Select Dataset", className="card-title", style={"font-weight":"bold"}),
+            dcc.Dropdown(
+                id='dataset-hm',
+                options=[{'label': i, 'value': i} for i in available_datasets],
+                # only passing in quant value columns
+                value=available_datasets[0]),
+
+                ])
+    ])
+
+first_card = html.Iframe(
+            id="cgrammer-iframe",
+            src="https://amp.pharm.mssm.edu/clustergrammer/viz/5eed203e8ec9bb2d622075e9/proteomics.txt",
+            height='600',
+            width='900',
+            style={"border-color":"transparent"}
+        )
+
+#app.layout = dbc.Container([
+layout = dbc.Container([
+
+    navbar,
+
+    html.Hr(),
+
+    dbc.Row(dbc.Col(html.H1("COVID-19 Multi-Omics Data Dashboard"), width={"size": 6, "offset": 3})),
+
+    html.Hr(),
+
+    dbc.Row(
+        [dbc.Col(
+        dbc.Nav(
+    [
+        html.H3("TYPE OF ANALYSIS", style={"font-weight":"bold", "color":"black"}),
+
+        dbc.NavItem(dbc.NavLink(html.Span("PCA"),
+            disabled=False,
+            href="pca",
+            style={"color":"grey"})),
+
+        dbc.NavItem(dbc.NavLink(
+
+            html.Span(
+                    "Linear Regression",
+                    id="tooltip-lr",
+                    style={"cursor": "pointer", "color":"grey"},
+                ),disabled=False, href="linear_regression")),
+        
+        dbc.NavItem(dbc.NavLink(
+
+            html.Span(
+                    "Differential Expression",
+                    id="tooltip-lr",
+                    style={"cursor": "pointer", "color":"grey"},
+                ),disabled=False, href="differential_expression")),
+
+        dbc.NavItem(dbc.NavLink("Clustergrammer", active=True, href="clustergrammer", style={"background-color":"grey"})),
+
+        html.Hr(),
+        control_panel
+    ],
+    vertical="md",
+    pills=True
+        ), md=2, className="mb-3"),
+
+        dbc.Col(first_card, md=7),
+        ],
+
+        className="mb-3"),
+
+], fluid=True, style={"height": "100vh"})
+
+
+@app.callback(
+    Output('cgrammer-iframe', 'src'),
+    [Input('dataset-hm', 'value')])
+def update_heatmap(dataset):
+
+    url_dict = {
+    "Proteins": "https://amp.pharm.mssm.edu/clustergrammer/viz/5eed203e8ec9bb2d622075e9/proteomics.txt",
+    "Lipids": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061a208ec9bb6fb2f14a1d/lipidomics.txt",
+    "Metabolites": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061c7f8ec9bb6fb2f14a37/metabolomics.txt",
+    "Transcripts": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061cfc8ec9bb6fb2f14a45/transcriptomics.txt"
+
+    }
+
+    url = url_dict[dataset]
+
+    return url
+
+if __name__ == '__main__':
+
+    import dash_bootstrap_components as dbc
+    external_stylesheets=[dbc.themes.BOOTSTRAP]
+
+    app = dash.Dash(
+        __name__,
+        external_stylesheets=external_stylesheets)
+    app.title = 'Clustergrammer'
+
+    app.layout = layout
+
+    @app.callback(
+        Output('cgrammer-iframe', 'src'),
+        [Input('dataset-hm', 'value')])
+    def update_heatmap(dataset):
+
+        url_dict = {
+        "Proteins": "https://amp.pharm.mssm.edu/clustergrammer/viz/5eed203e8ec9bb2d622075e9/proteomics.txt",
+        "Lipids": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061a208ec9bb6fb2f14a1d/lipidomics.txt",
+        "Metabolites": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061c7f8ec9bb6fb2f14a37/metabolomics.txt",
+        "Transcripts": "https://amp.pharm.mssm.edu/clustergrammer/viz/5f061cfc8ec9bb6fb2f14a45/transcriptomics.txt"
+
+        }
+
+        url = url_dict[dataset]
+
+        return url
+
+    app.run_server(
+        debug=True,
+        host='0.0.0.0',
+        #port='8080'
+        )