Update OncoMark.py

OncoMark.py

@@ -1,193 +1,195 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-from scipy.stats import rankdata
-import time
-from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
-import matplotlib.pyplot as plt
-import io
-from ulm import run_ulm
-from mlm import run_mlm
-from plotting import plot_barplot
-import tensorflow as tf
-import os
-
-# Page Configuration
-st.set_page_config(page_title="OncoMark", layout="wide")
-st.image("oncomark_title.png", caption="", use_container_width=True)
-# st.title("OncoMark")
-
-# Sidebar for uploading data
-st.sidebar.header("Upload Data")
-uploaded_file = st.sidebar.file_uploader("Upload your data file (CSV)", type=["csv"])
-
-# Description and Instructions
-# st.write("AI to predict cancer hallmarks from transcriptomics data.")
-
-# Load model
-model_path = 'hallmark_model.keras'
-scaler_path = 'hallmark_scaler.joblib'
-feature_file = 'hallmark_feature.txt'
-
-# Load the pre-trained model and scaler
-model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__), model_path))
-scaler = joblib.load(os.path.join(os.path.dirname(__file__), scaler_path))
-
-# Load feature names
-with open((os.path.join(os.path.dirname(__file__), feature_file)), 'r') as file:
-    feature_names = file.read().splitlines()
-
-# Define hallmark tasks
-hall_list = ['AIM', 'DCE', 'EGS', 'GIM', 'RCD', 'SPS', 'AID', 'IA', 'ERI', 'TPI']
-collectri = pd.read_csv('collectri_df.csv')
-progeny = pd.read_csv('progeny_df.csv')
-
-# Show an example structure if no data is uploaded
-if uploaded_file is not None:
-    data = pd.read_csv(uploaded_file, index_col=0)
-    tf_acts, tf_pvals = run_ulm(mat=data, net=collectri, verbose=False)
-    pathway_acts, pathway_pvals = run_mlm(mat=data, net=progeny, verbose=False)
-
-    st.write("### Uploaded Data")
-    st.write(data.iloc[:5, :50])
-    data = data.loc[:, ~data.columns.duplicated(keep='first')]
-    data = data.reindex(columns=feature_names, fill_value=0).fillna(0)
-    data_index = data.index
-    data = rankdata(data * -1, axis=1, method='average')
-    data = np.log2(data)
-    data = scaler.transform(data)
-else:
-    st.write("### Example Input Format")
-    st.info("**Note:** I am flexible and can handle both normalized and non-normalized input data. Upload your data as is, and the model will adjust accordingly to provide accurate predictions.")
-    raw_count_data = pd.DataFrame({
-        'GeneA': [120, 150, 80],
-        'GeneB': [200, 180, 190],
-        'GeneC': [90, 75, 110],
-        'GeneD': [60, 95, 100]
-    }, index=['Sample1', 'Sample2', 'Sample3'])
-    st.write(raw_count_data)
-
-# Dummy model function (replace with actual model prediction)
-def model_predict(input_data):
-    predictions = model.predict(data)
-    prediction_df = pd.DataFrame()
-    for task_id, hall_name in enumerate(hall_list):
-        prediction_df[hall_name] = predictions[task_id].flatten()
-    prediction_df.index = data_index
-    return prediction_df
-
-def display_loading_animation():
-    with st.empty():
-        for i in range(3):
-            st.write("🔍 Predicting" + "." * (i + 1))
-            time.sleep(1.0)
-        st.write("🚀 Almost there...")
-
-# Initialize predictions to None
-predictions = None
-
-# Predict and display results if data is uploaded
-if uploaded_file is not None:
-    st.write("### Predictions")
-    display_loading_animation()
-    predictions = model_predict(data)
-    predictions = predictions.reset_index()
-    # st.write(predictions)
-else:
-    st.write("### Predictions")
-    st.info("Upload your data to see predictions.")
-
-
-selected = None
-
-# Display analysis if predictions are available
-if predictions is not None:
-    # Display predictions in AgGrid
-    gb = GridOptionsBuilder.from_dataframe(predictions)
-    gb.configure_selection(selection_mode='single', use_checkbox=False)
-    gb.configure_default_column(resizable=True, autoWidth=True, maxWidth=100)
-    grid_options = gb.build()
-
-    grid_response = AgGrid(
-        predictions,
-        gridOptions=grid_options,
-        update_mode=GridUpdateMode.SELECTION_CHANGED,
-        height=300,
-        enable_enterprise_modules=False,
-        allow_unsafe_jscode=True,
-        theme='streamlit',
-        custom_css={
-            ".ag-row-selected": {
-                "background-color": "#90EE90 !important"
-            }
-        }
-    )
-
-    csv_grid = predictions.to_csv().encode('utf-8')
-    st.download_button(
-        label="Download Table as CSV",
-        data=csv_grid,
-        file_name='aggrid_table.csv',
-        mime='text/csv'
-    )
-
-    # Extract selected row data and display bar plot on selection
-    selected = grid_response['selected_rows']
-    if selected is not None:
-        st.write("### Analysis")
-        selected_df = pd.DataFrame(selected)
-        sample_name = selected_df['index'][0]
-
-        st.write('##### Transcription factor activity')
-        st.info('If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.')
-        plot_barplot(
-        acts=tf_acts,
-        contrast=sample_name,
-        top=50,
-        vertical=False,
-        figsize=(11, 5))
-        buf = io.BytesIO()
-        plt.savefig(buf, format='png', dpi=300)
-        buf.seek(0)
-        st.pyplot(plt)
-        # Provide option to download the plot
-        st.download_button(
-            label="Download Plot as PNG",
-            data=buf,
-            file_name='tf_hallmark_{}.png'.format(sample_name),
-            mime='image/png'
-        )
-
-        st.write('##### Pathway activity')
-        st.info('If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.')
-        plot_barplot(
-        pathway_acts,
-        sample_name,
-        top=50,
-        vertical=False,
-        figsize=(6, 3))
-        buf = io.BytesIO()
-        plt.savefig(buf, format='png', dpi=300)
-        buf.seek(0)
-        st.pyplot(plt)
-        # Provide option to download the plot
-        st.download_button(
-            label="Download Plot as PNG",
-            data=buf,
-            file_name='pathway_hallmark_{}.png'.format(sample_name),
-            mime='image/png'
-        )
-
-    else:
-        st.write("### Analysis")
-        st.info('Click on a sample under predictions to see the analysis')
-else:
-    st.write("### Analysis")
-    st.info('Click on a sample under predictions to see the analysis')
-
-# Footer
-st.write("----")
-st.markdown("[Visit our GitHub Repository](https://github.com/SML-CompBio/OncoMark)", unsafe_allow_html=True)
-
-# Running the app: use `streamlit run filename.py`
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from scipy.stats import rankdata
+import time
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+import matplotlib.pyplot as plt
+import io
+from ulm import run_ulm
+from mlm import run_mlm
+from plotting import plot_barplot
+import tensorflow as tf
+import os
+
+# Page Configuration
+st.set_page_config(page_title="OncoMark", layout="wide")
+st.image("oncomark_title.png", caption="", use_container_width=True)
+st.markdown("[📘 See Tutorial Guide](https://oncomark.readthedocs.io/en/latest/usage/)", unsafe_allow_html=True)
+# st.title("OncoMark")
+
+# Sidebar for uploading data
+st.sidebar.header("Upload Data")
+uploaded_file = st.sidebar.file_uploader("Upload your data file (CSV)", type=["csv"])
+st.sidebar.markdown("[Need help? View tutorial](https://oncomark.readthedocs.io/en/latest/usage/)", unsafe_allow_html=True)
+
+# Description and Instructions
+# st.write("AI to predict cancer hallmarks from transcriptomics data.")
+
+# Load model
+model_path = 'hallmark_model.keras'
+scaler_path = 'hallmark_scaler.joblib'
+feature_file = 'hallmark_feature.txt'
+
+# Load the pre-trained model and scaler
+model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__), model_path))
+scaler = joblib.load(os.path.join(os.path.dirname(__file__), scaler_path))
+
+# Load feature names
+with open((os.path.join(os.path.dirname(__file__), feature_file)), 'r') as file:
+    feature_names = file.read().splitlines()
+
+# Define hallmark tasks
+hall_list = ['AIM', 'DCE', 'EGS', 'GIM', 'RCD', 'SPS', 'AID', 'IA', 'ERI', 'TPI']
+collectri = pd.read_csv('collectri_df.csv')
+progeny = pd.read_csv('progeny_df.csv')
+
+# Show an example structure if no data is uploaded
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file, index_col=0)
+    tf_acts, tf_pvals = run_ulm(mat=data, net=collectri, verbose=False)
+    pathway_acts, pathway_pvals = run_mlm(mat=data, net=progeny, verbose=False)
+
+    st.write("### Uploaded Data")
+    st.write(data.iloc[:5, :50])
+    data = data.loc[:, ~data.columns.duplicated(keep='first')]
+    data = data.reindex(columns=feature_names, fill_value=0).fillna(0)
+    data_index = data.index
+    data = rankdata(data * -1, axis=1, method='average')
+    data = np.log2(data)
+    data = scaler.transform(data)
+else:
+    st.write("### Example Input Format")
+    st.info("**Note:** I am flexible and can handle both normalized and non-normalized input data. Upload your data as is, and the model will adjust accordingly to provide accurate predictions.")
+    raw_count_data = pd.DataFrame({
+        'GeneA': [120, 150, 80],
+        'GeneB': [200, 180, 190],
+        'GeneC': [90, 75, 110],
+        'GeneD': [60, 95, 100]
+    }, index=['Sample1', 'Sample2', 'Sample3'])
+    st.write(raw_count_data)
+
+# Dummy model function (replace with actual model prediction)
+def model_predict(input_data):
+    predictions = model.predict(data)
+    prediction_df = pd.DataFrame()
+    for task_id, hall_name in enumerate(hall_list):
+        prediction_df[hall_name] = predictions[task_id].flatten()
+    prediction_df.index = data_index
+    return prediction_df
+
+def display_loading_animation():
+    with st.empty():
+        for i in range(3):
+            st.write("🔍 Predicting" + "." * (i + 1))
+            time.sleep(1.0)
+        st.write("🚀 Almost there...")
+
+# Initialize predictions to None
+predictions = None
+
+# Predict and display results if data is uploaded
+if uploaded_file is not None:
+    st.write("### Predictions")
+    display_loading_animation()
+    predictions = model_predict(data)
+    predictions = predictions.reset_index()
+    # st.write(predictions)
+else:
+    st.write("### Predictions")
+    st.info("Upload your data to see predictions.")
+
+
+selected = None
+
+# Display analysis if predictions are available
+if predictions is not None:
+    # Display predictions in AgGrid
+    gb = GridOptionsBuilder.from_dataframe(predictions)
+    gb.configure_selection(selection_mode='single', use_checkbox=False)
+    gb.configure_default_column(resizable=True, autoWidth=True, maxWidth=100)
+    grid_options = gb.build()
+
+    grid_response = AgGrid(
+        predictions,
+        gridOptions=grid_options,
+        update_mode=GridUpdateMode.SELECTION_CHANGED,
+        height=300,
+        enable_enterprise_modules=False,
+        allow_unsafe_jscode=True,
+        theme='streamlit',
+        custom_css={
+            ".ag-row-selected": {
+                "background-color": "#90EE90 !important"
+            }
+        }
+    )
+
+    csv_grid = predictions.to_csv().encode('utf-8')
+    st.download_button(
+        label="Download Table as CSV",
+        data=csv_grid,
+        file_name='aggrid_table.csv',
+        mime='text/csv'
+    )
+
+    # Extract selected row data and display bar plot on selection
+    selected = grid_response['selected_rows']
+    if selected is not None:
+        st.write("### Analysis")
+        selected_df = pd.DataFrame(selected)
+        sample_name = selected_df['index'][0]
+
+        st.write('##### Transcription factor activity')
+        st.info('If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.')
+        plot_barplot(
+        acts=tf_acts,
+        contrast=sample_name,
+        top=50,
+        vertical=False,
+        figsize=(11, 5))
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=300)
+        buf.seek(0)
+        st.pyplot(plt)
+        # Provide option to download the plot
+        st.download_button(
+            label="Download Plot as PNG",
+            data=buf,
+            file_name='tf_hallmark_{}.png'.format(sample_name),
+            mime='image/png'
+        )
+
+        st.write('##### Pathway activity')
+        st.info('If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.')
+        plot_barplot(
+        pathway_acts,
+        sample_name,
+        top=50,
+        vertical=False,
+        figsize=(6, 3))
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=300)
+        buf.seek(0)
+        st.pyplot(plt)
+        # Provide option to download the plot
+        st.download_button(
+            label="Download Plot as PNG",
+            data=buf,
+            file_name='pathway_hallmark_{}.png'.format(sample_name),
+            mime='image/png'
+        )
+
+    else:
+        st.write("### Analysis")
+        st.info('Click on a sample under predictions to see the analysis')
+else:
+    st.write("### Analysis")
+    st.info('Click on a sample under predictions to see the analysis')
+
+# Footer
+st.write("----")
+st.markdown("[Visit our GitHub Repository](https://github.com/SML-CompBio/OncoMark)", unsafe_allow_html=True)
+
+# Running the app: use `streamlit run filename.py`