From 9679d19cd261eade41f551b63aac79d43c978bcd Mon Sep 17 00:00:00 2001 From: Eduardo Reyes <62916582+EdRey05@users.noreply.github.com> Date: Thu, 14 Mar 2024 15:09:31 -0400 Subject: [PATCH] Version 11 - Complete V11 (Mar 14, 2024): Some more exceptions handled. When the clinical and RNA patient IDs do not match, and when there are NaNs in the time to event column. The logging has not been revised yet. --- 003_KM_plotter/app.py | 90 ++++++++++++++++++++++++++++--------------- 1 file changed, 58 insertions(+), 32 deletions(-) diff --git a/003_KM_plotter/app.py b/003_KM_plotter/app.py index 25b98d9..8877ee0 100644 --- a/003_KM_plotter/app.py +++ b/003_KM_plotter/app.py @@ -3,11 +3,11 @@ Eduardo Reyes Alvarez, Ph.D. Contact: eduardo_reyes09@hotmail.com +Done: CS, FEZF1, GRK7, MSH5, NOS3, NTHL1, RUVBL1, SELPLG, SEMA3A, TNK2, UBR5 App version: - V10 (Jan 06, 2024): Major improvements to -Using variable(s)- option. Now the content of each - selected column/variable is analyzed better in base of 9 observed scenarios - to display the most appropriate plot and subgrouping widgets. The logging + V11 (Mar 14, 2024): Some more exceptions handled. When the clinical and RNA patient IDs do not + match, and when there are NaNs in the time to event column. The logging has not been revised yet. ''' ################################################################################################### @@ -42,8 +42,8 @@ # Use these colors for altair charts st.session_state["alt_colors"] = ["#76448A", "#B03A2E", "#1E8449", "#1F618D", "#34495E ", - "#D68910", "#707B7C", "#E67E22", "#2E86C1", "#E74C3C", - "#2C3E50", "#F1C40F", "#3498DB", "#D35400", "#27AE60"] + "#D68910", "#707B7C", "#E67E22", "#2E86C1", "#E74C3C", + "#2C3E50", "#F1C40F", "#3498DB", "#D35400", "#27AE60"] # Title st.title("Interactive Kaplan-Meier plot generator") st.markdown('
', unsafe_allow_html=True) @@ -51,7 +51,7 @@ # Sidebar - Initial widgets with st.sidebar: uploaded_files = st.file_uploader(label="Upload a clinical file (and optionally, a RNA file)", - type=["txt"], accept_multiple_files=True) + type=["txt"], accept_multiple_files=True) st.markdown('
', unsafe_allow_html=True) start_button = st.button(label="Begin", type="secondary") restart_button = st.button(label="Start over", type="secondary") @@ -60,9 +60,9 @@ # Function to setup the logging configuration def logging_setup(): - - # Check if logging has already been initialized - if "log_created" not in st.session_state: + + # Check if logging has already been initialized + if "log_created" not in st.session_state: # Configure the logging settings logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") @@ -99,7 +99,7 @@ def load_input_files(uploaded_files): # Check if one or two files were uploaded clinical_file = next((file for file in uploaded_files if file.name == "clinical.txt"), None) RNA_file = next((file for file in uploaded_files if file.name == "RNA.txt"), None) - + # Load the files available if clinical_file is not None: @@ -208,9 +208,9 @@ def file_preprocessing(): # Save the neccesary data for the next steps in the session state st.session_state.update({"df_clinical": df_clinical, - "df_RNA": df_RNA, - "time_to_event_options": time_to_event_options, - "event_observation_options": event_observation_options}) + "df_RNA": df_RNA, + "time_to_event_options": time_to_event_options, + "event_observation_options": event_observation_options}) # Also add the options of genes if a RNA file was uploaded if df_RNA is not None: @@ -239,10 +239,10 @@ def widget_preparation(): # Save the columns and containers in the session state widget_and_output_areas = [col_1_row_1, col_2_row_1, - col_1_row_2, col_2_row_2, - col_1_row_3, col_2_row_3, col_3_row_3, col_4_row_3, - col_1_row_14, col_2_row_14, col_3_row_14, - col_1_row_15, col_2_row_15, col_3_row_15, col_4_row_15] + col_1_row_2, col_2_row_2, + col_1_row_3, col_2_row_3, col_3_row_3, col_4_row_3, + col_1_row_14, col_2_row_14, col_3_row_14, + col_1_row_15, col_2_row_15, col_3_row_15, col_4_row_15] st.session_state["widget_and_output_areas"] = widget_and_output_areas # Time to event widget and callback function @@ -341,26 +341,31 @@ def time_to_event_dropdown_handler(change): time_column = df_clinical[column_name].dropna() logger.info(f"The user selected: {column_name} Widget: time_to_event_dropdown. \n") logger.info(f"Original dtype of {column_name}: {df_clinical[column_name].dtype} Dtype once removing NANs: {time_column.dtype} \n") - + # Make histogram of values with altair and handle exceptions - if time_column.dtype != "object": - alt_data1 = pd.DataFrame({column_name: time_column}) + if time_column.dtype == "object": + # First try to convert the column to a numeric type and if it fails we show a warning + try: + time_column = pd.to_numeric(time_column, errors="coerce") + time_column = time_column.dropna() + except: + info_str = "Warning: Column type is not numeric." + logger.warning("User attention required: The time to event column may not be numerical. \n") + + with time_to_event_output: + time_to_event_output.empty() + st.warning(info_str) + st.stop() - chart1 = alt.Chart(alt_data1).mark_bar(color="#BA4A00").encode( + alt_data1 = pd.DataFrame({column_name: time_column}) + + chart1 = alt.Chart(alt_data1).mark_bar(color="#BA4A00").encode( alt.X(column_name, type="quantitative", bin=alt.Bin(step=12)), alt.Y("count()", title="Patients"), ).properties(width=425, height=325 ).configure_axis(labelColor="#3386BD") - logger.info(f"A histogram was successfully made and displayed for: {column_name} \n") - else: - # If the column is not numeric, display a warning and stop the app - info_str = "Warning: Column type is not numeric." - logger.warning("User attention required: The time to event column may not be numerical. \n") + logger.info(f"A histogram was successfully made and displayed for: {column_name} \n") - with time_to_event_output: - time_to_event_output.empty() - st.warning(info_str) - st.stop() else: # If the column is not in the df, display an error and stop the app info_str = "Warning: Column not found in the dataframe." @@ -496,7 +501,7 @@ def subgroup_buttons_handler(change): # Show a slider for 1 to 5 variables (to prevent crazy number of curves) with subgroup_buttons_output: variable_number_slider = st.slider(label="Number of variables:", - min_value=1, max_value=5, step=1, value=1) + min_value=1, max_value=5, step=1, value=1) variable_number_slider_handler(variable_number_slider) else: # If the user doesn't want to make subgroups, don't show the slider @@ -1317,6 +1322,13 @@ def variables_selection_handler(change, repeat): logger.info(f"[Subgrouping 2nd step] The column {change} -{KM_data_1var.dtypes[change]} dtype- from df_RNA was selected to make subgroups. \n") column_data[repeat - 1] = KM_data_1var[change].copy() + # Raise a warning when the patient IDs do not match in the clinical and RNA datasets, causing an empty df + if len(KM_data_1var) == 0: + logger.warning(f"[Subgrouping 2nd step] The merge of datasets produced an empty dataframe, it is likely that the IDs may not match. \n") + st.error("The merge of datasets produced an empty dataframe, check that the patient IDs match in both datasets. \n") + st.error("You may need to check the data source or pre-process the datasets to make the IDs consistent before using this app.") + st.stop() + # Log the current status of KM_data_1var logger.info(f"[Subgrouping 2nd step] Keep relevant columns of KM_data_1var and only rows with 0/1 event labels: \n {KM_data_1var.iloc[:15, :10].to_string()} \n") logger.info(f"[Subgrouping 2nd step] Data types of KM_data_1var columns: \n {KM_data_1var.dtypes.to_string()} \n\n") @@ -1454,6 +1466,16 @@ def pass_KM_parameters(): KM_plot_area.empty() st.warning("First select the values to label as 0 and 1 (No event, event)!!") st.stop() + + # Check if the time to event column is numerical + if df_clinical[time_to_event_selection].dtype == "object": + # First try to convert the column to a numeric type and if it fails we show a warning + try: + df_clinical[time_to_event_selection] = pd.to_numeric(df_clinical[time_to_event_selection], errors="coerce") + df_clinical[time_to_event_selection] = df_clinical[time_to_event_selection].dropna() + except: + st.warning("Warning: Time to Event column is not numeric.") + st.stop() # If no subgrouping is required, apply the event tags and pass the data to KM_analysis if subgroup_buttons_selection == "None": @@ -1700,6 +1722,10 @@ def KM_analysis(KM_data, KM_subgroups): # Create KaplanMeierFitter objects for each subgroup in KM_subgroups for label, subset in KM_subgroups.items(): + + # Remove any rows in the subset with NaN values + subset = subset.dropna() + kmf = KaplanMeierFitter() kmf.fit(durations=subset[current_time_column], event_observed=subset[current_event_column]) KMF_object[label] = kmf @@ -1741,7 +1767,7 @@ def save_KM_results(generate_plot_button): # Prepare the data to be processed (single KM object or list of KM objects) if isinstance(KM_analysis_output, dict): KM_objects_to_process = [{"label": f"KM_Subgroup_{i+1}", "KM_object": KM_object} - for i, (label, KM_object) in enumerate(KM_analysis_output.items())] + for i, (label, KM_object) in enumerate(KM_analysis_output.items())] real_labels = [f"KM_Subgroup_{i+1}: {label}" for i, (label, KM_object) in enumerate(KM_analysis_output.items())] else: # If KM_analysis_output is a single KM object, add it to the list as a dictionary with a general label