--- a
+++ b/src/dash/apps/pca.py
@@ -0,0 +1,309 @@
+
+import dash
+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
+
+from data import get_omics_data, get_biomolecule_names, get_combined_data
+from plot import biomolecule_bar, boxplot, pca_scores_plot, pca_loadings_plot
+from nav import navbar
+
+external_stylesheets=[dbc.themes.BOOTSTRAP]
+
+"""app = dash.Dash(
+    __name__,
+    external_stylesheets=external_stylesheets)
+app.title = 'COVID-19 Multi-Omics'"""
+
+from app import app
+
+print()
+print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+print("Loading data for pca...")
+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', 'Combined Biomolecules']
+
+# define dataset dictionaries
+from app import dataset_dict, df_dict, quant_value_range_dict, global_names_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
+
+# get combined omics df and quant value range
+print("Creating combined omics df...")
+df_dict, quant_value_range_dict = get_combined_data(df_dict, quant_value_range_dict)
+
+# start with proteomics data
+sorted_biomolecule_names_dict = {k: v for k, v in sorted(proteomics_biomolecule_names_dict.items(), key=lambda item: item[1])}
+#available_biomolecules = proteomics_biomolecule_names_dict.values()
+#available_biomolecules = proteomics_df.columns[:proteomics_quant_range].sort_values().tolist()
+default_biomolecule = list(sorted_biomolecule_names_dict.keys())[0]
+
+plotly_config = {"toImageButtonOptions":{'format':'svg',
+                'filename': 'dash_plot'},
+                "displaylogo": False}
+
+first_card = dbc.Card(
+    [
+        dbc.CardHeader("PCA SCORES PLOT",
+                            style={"background-color":"#5bc0de",
+                                    "font-weight":"bold",
+                                    "font-size":"large"}),
+        dbc.CardBody(dcc.Graph(id='pca-scores-figure',
+            config=plotly_config))
+
+        ])
+
+second_card = dbc.Card(
+    [
+        dbc.CardHeader("PCA LOADINGS PLOT",
+                            style={"background-color":"#5bc0de",
+                                    "font-weight":"bold",
+                                    "font-size":"large"}),
+        dbc.CardBody(dcc.Graph(id='pca-loadings-figure',
+        config=plotly_config))
+    ])
+
+third_card = dbc.Card(
+    [
+        dbc.CardHeader("BIOMOLECULE BARPLOT",
+                            style={"background-color":"#5bc0de",
+                                        "font-weight":"bold",
+                                        "font-size":"large"}),
+        dbc.CardBody(dcc.Graph(id='biomolecule-barplot',
+        config=plotly_config))
+    ])
+
+fourth_card = dbc.Card(
+    [
+        dbc.CardHeader("BIOMOLECULE BOXPLOT",
+                            style={"background-color":"#5bc0de",
+                                    "font-weight":"bold",
+                                    "font-size":"large"}),
+        dbc.CardBody(dcc.Graph(id='biomolecule-boxplot',
+        config=plotly_config))
+    ])
+
+###
+
+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_id',
+                options=[{'label': i, 'value': i} for i in available_datasets],
+                # only passing in quant value columns
+                value=available_datasets[0]),
+            html.Hr(),
+            html.P("Select Biomolecule", className="card-title", style={"font-weight":"bold"}),
+
+            # NOTE: This is dcc object not dbc
+            dcc.Dropdown(
+                id='biomolecule_id',
+                # label maps to biomolecule name, value to biomolecule_id
+                options=[{'label': value, 'value': key} for key, value in sorted_biomolecule_names_dict.items()],
+                # only passing in quant value columns
+                value=default_biomolecule,
+                className="dropdown-item p-0"),
+
+                ])
+    ])
+
+#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("PCA", active=True, href="pca", style={"background-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-de",
+                    style={"cursor": "pointer", "color":"grey"},
+                ),disabled=False, href="differential_expression")),
+
+                dbc.NavItem(dbc.NavLink(
+                    html.Span(
+                            "Clustergrammer",
+                            id="tooltip-cg",
+                            style={"cursor":"pointer", "color":"grey"},
+                        ),disabled=False, href="clustergrammer")),
+
+        html.Hr(),
+        control_panel
+    ],
+    vertical="md",
+    pills=True
+        ), md=2, className="mb-3"),
+
+        #dbc.Col(control_panel, md=6)
+        dbc.Col(first_card, md=4),
+        dbc.Col(second_card, md=6)
+        ],
+
+        className="mb-3"),
+
+    dbc.Row([dbc.Col(third_card, md=7, align="center"), dbc.Col(fourth_card, md=5, align="center")], className="mb-3")
+
+], fluid=True)
+
+
+@app.callback(
+    dash.dependencies.Output('biomolecule_id', 'options'),
+    [Input('dataset_id', 'value')])
+def update_biomolecule_options(dataset_id):
+
+    dataset = dataset_dict[dataset_id]
+    biomolecule_names_dict = global_names_dict[dataset]
+
+    df = df_dict[dataset]
+    quant_value_range = quant_value_range_dict[dataset]
+
+    # get list of columns for dataset
+    available_biomolecules = df.columns[:quant_value_range].sort_values().tolist()
+
+    sorted_biomolecule_names_dict = {k: v for k, v in sorted(biomolecule_names_dict.items(), key=lambda item: item[1])}
+
+    options=[{'label': value, 'value': key} for key, value in sorted_biomolecule_names_dict.items() if key in available_biomolecules]
+    #print(options)
+    return options
+
+@app.callback(
+    Output('biomolecule_id', 'value'),
+    [Input('dataset_id', 'value')])
+def update_default_biomolecule(dataset_id):
+
+    dataset = dataset_dict[dataset_id]
+    biomolecule_names_dict = global_names_dict[dataset]
+
+    sorted_biomolecule_names_dict = {k: v for k, v in sorted(biomolecule_names_dict.items(), key=lambda item: item[1])}
+    default_biomolecule=list(sorted_biomolecule_names_dict.keys())[0]
+
+    return default_biomolecule
+
+@app.callback(
+    Output('pca-scores-figure', 'figure'),
+    [Input('dataset_id', 'value')])
+def update_pca_scores_plot(dataset_id):
+
+    dataset = dataset_dict[dataset_id]
+    df = df_dict[dataset]
+    quant_value_range = quant_value_range_dict[dataset]
+
+    fig = pca_scores_plot(df, quant_value_range)
+
+    return fig
+
+@app.callback(
+    Output('pca-loadings-figure', 'figure'),
+    [Input('dataset_id', 'value'),
+    Input('biomolecule_id', 'value')])
+def update_pca_loadings_plot(dataset_id, biomolecule_id):
+
+    dataset = dataset_dict[dataset_id]
+    df = df_dict[dataset]
+    biomolecule_names_dict = global_names_dict[dataset]
+    quant_value_range = quant_value_range_dict[dataset]
+
+    # build ome type list for coloring
+    if not dataset == 'combined':
+        ome_type_list = [dataset] * quant_value_range
+
+    else:
+        ome_type_list = ['proteomics'] * quant_value_range_dict['proteomics']
+        ome_type_list.extend(['lipidomics'] * quant_value_range_dict['lipidomics'])
+        ome_type_list.extend(['metabolomics'] * quant_value_range_dict['metabolomics'])
+        ome_type_list.extend(['transcriptomics'] * quant_value_range_dict['transcriptomics'])
+
+    # get biomolecule index
+    biomolecule_index = df.columns.tolist().index(biomolecule_id)
+    ome_type_list[biomolecule_index] = 'selected_biomolecule'
+
+    fig = pca_loadings_plot(df, quant_value_range, dataset_id, biomolecule_names_dict, ome_type_list)
+
+    return fig
+
+@app.callback(
+    Output('biomolecule-barplot', 'figure'),
+    [Input('biomolecule_id', 'value'),
+    Input('dataset_id', 'value')])
+def update_biomolecule_barplot(biomolecule_id, dataset_id):
+
+    dataset = dataset_dict[dataset_id]
+    df = df_dict[dataset]
+
+    biomolecule_names_dict = global_names_dict[dataset]
+    biomolecule_name = biomolecule_names_dict[biomolecule_id]
+
+    fig = biomolecule_bar(df, biomolecule_id, biomolecule_names_dict)
+
+    return fig
+
+@app.callback(
+    Output('biomolecule-boxplot', 'figure'),
+    [Input('biomolecule_id', 'value'),
+    Input('dataset_id', 'value')])
+def update_biomolecule_boxplot(biomolecule_id, dataset_id):
+
+    dataset = dataset_dict[dataset_id]
+    df = df_dict[dataset]
+
+    biomolecule_names_dict = global_names_dict[dataset]
+    biomolecule_name = biomolecule_names_dict[biomolecule_id]
+
+    fig = boxplot(df, biomolecule_id, biomolecule_names_dict)
+
+    return fig
+
+print("Starting server...")
+
+if __name__ == '__main__':
+    app.run_server(
+        debug=True,
+        host='0.0.0.0',
+        #port='8080'
+        )