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import pandas as pd
import gradio as gr
from .visualization import QCVisualizer, AnalysisVisualizer
class ComponentHelper:
def __init__(self,
file_qc_results: pd.DataFrame,
dataset_qc_results: pd.DataFrame,
analyzed_gating_results: pd.DataFrame,
analyzed_gating_results_wCDAResults: pd.DataFrame,
config: dict):
self.file_qc_results = file_qc_results
self.dataset_qc_results = dataset_qc_results
self.analyzed_gating_results = analyzed_gating_results
self.analyzed_gating_results_wCDAResults = analyzed_gating_results_wCDAResults
self.config = config
self.choices = {"dataset_filter": list(file_qc_results["Dataset"].unique()),
"model_filter": sorted(list(file_qc_results["Instrument model"].unique())),
"sop_exp_qc_tab_filter": list(file_qc_results["SOP-Exp"].unique()),
"material_filter": list((file_qc_results["SOP-Exp"] + " " + file_qc_results["Material"]).unique()),
"issue_filter": config["qc_results_tab"]["issues"],
"sample_filter": list(analyzed_gating_results["Sample"].unique()),
"sop_exp_analysis_tab_filter": list(analyzed_gating_results["SOP-Exp"].unique()),
"compensation_control_filter": list(analyzed_gating_results["Compensation control"].unique()),
"gating_control_filter": list(analyzed_gating_results["Gating control"].unique()),
"pop_pheno_parent_filter": list(analyzed_gating_results["Population; Phenotype (Parent gate)"].unique()),
"analyzed_result_filter": config["gating_results_tab"]["results"],
}
self.creator = self._Creator(self)
self.updater = self._Updater(self)
class _Creator:
def __init__(self,
helper_instance):
self.helper = helper_instance
self.initialization_values = {}
def main_filter_components(self):
with gr.Row():
gr.Markdown("Instrument model")
clear_all_models_button = gr.Button("Clear All", size="sm")
select_all_models_button = gr.Button("Select All", size="sm")
model_filter = gr.Dropdown(label='', multiselect=True,
choices=self.helper.choices["model_filter"],
value=self.helper.choices["model_filter"])
self.initialization_values["model_filter"] = self.helper.choices["model_filter"]
with gr.Row():
gr.Markdown("Dataset")
clear_all_datasets_button = gr.Button("Clear All", size="sm")
select_all_datasets_button = gr.Button(
"Select All", size="sm")
dataset_filter = gr.Dropdown(label='', multiselect=True,
choices=self.helper.choices["dataset_filter"],
value=self.helper.choices["dataset_filter"])
self.initialization_values["dataset_filter"] = self.helper.choices["dataset_filter"]
main_apply_filters_button = gr.Button(value="Apply filters")
return clear_all_models_button, select_all_models_button, model_filter, \
clear_all_datasets_button, select_all_datasets_button, dataset_filter, \
main_apply_filters_button
def qc_tab_filter_components(self):
with gr.Row():
gr.Markdown("SOP-Exp")
clear_all_sop_exps_qc_tab_button = gr.Button(
"Clear All", size="sm")
select_all_sop_exps_qc_tab_button = gr.Button(
"Select All", size="sm")
sop_exp_qc_tab_filter = gr.Dropdown(label='', multiselect=True,
choices=self.helper.choices["sop_exp_qc_tab_filter"],
value=self.helper.choices["sop_exp_qc_tab_filter"])
self.initialization_values["sop_exp_qc_tab_filter"] = self.helper.choices["sop_exp_qc_tab_filter"]
with gr.Row():
gr.Markdown("Material")
clear_all_materials_button = gr.Button("Clear All", size="sm")
select_all_materials_button = gr.Button(
"Select All", size="sm")
material_filter = gr.Dropdown(label='', multiselect=True,
choices=self.helper.choices["material_filter"],
value=self.helper.choices["material_filter"])
self.initialization_values["material_filter"] = self.helper.choices["material_filter"]
with gr.Row():
gr.Markdown("Issues")
clear_all_issues_button = gr.Button("Clear All", size="sm")
select_all_issues_button = gr.Button("Select All", size="sm")
issue_filter = gr.Dropdown(label='', multiselect=True,
choices=self.helper.choices["issue_filter"],
value=self.helper.choices["issue_filter"])
self.initialization_values["issue_filter"] = self.helper.choices["issue_filter"]
qc_tab_apply_filters_button = gr.Button(value="Apply filters")
return clear_all_sop_exps_qc_tab_button, select_all_sop_exps_qc_tab_button, sop_exp_qc_tab_filter, \
clear_all_materials_button, select_all_materials_button, material_filter, \
clear_all_issues_button, select_all_issues_button, issue_filter, \
qc_tab_apply_filters_button
def qc_tab_dataset_qc_status_filter_component(self):
gr.Markdown("Dataset QC status")
qc_tab_dataset_qc_status_filter = gr.CheckboxGroup(label='',
choices=[
"Pass QC", "Fail QC"],
value=["Pass QC", "Fail QC"])
self.initialization_values["qc_tab_dataset_qc_status_filter"] = [
"Pass QC", "Fail QC"]
return qc_tab_dataset_qc_status_filter
def file_qc_result_components(self):
gr.Markdown("File QC status")
file_qc_status_filter = gr.CheckboxGroup(label='',
choices=[
"Pass QC", "Fail QC"],
value=["Fail QC"])
self.initialization_values["file_qc_status_filter"] = ["Fail QC"]
updater = self.helper.updater
updater.update_file_qc_table(self.initialization_values["dataset_filter"],
self.initialization_values["sop_exp_qc_tab_filter"],
self.initialization_values["material_filter"],
self.initialization_values["issue_filter"],
self.initialization_values["qc_tab_dataset_qc_status_filter"],
self.initialization_values["file_qc_status_filter"])
file_qc_table = gr.Dataframe(
type="pandas",
show_copy_button=True, show_row_numbers=True,
value=updater.filtered_file_qc_results)
file_qc_table_no_file_msg = gr.Markdown(
value="All files are filtered", visible=False)
return file_qc_status_filter, file_qc_table, file_qc_table_no_file_msg
def dataset_qc_result_components(self):
updater = self.helper.updater
updater.update_file_qc_table(self.initialization_values["dataset_filter"],
self.initialization_values["sop_exp_qc_tab_filter"],
self.initialization_values["material_filter"],
self.initialization_values["issue_filter"],
self.initialization_values["qc_tab_dataset_qc_status_filter"],
self.initialization_values["file_qc_status_filter"])
dataset_qc_table = gr.Dataframe(
type="pandas",
show_copy_button=True, show_row_numbers=True,
value=updater.filtered_dataset_qc_results)
dataset_qc_table_no_dataset_msg = gr.Markdown(
value="All datasets are filtered", visible=False)
return dataset_qc_table, dataset_qc_table_no_dataset_msg
def qc_result_visual_components(self):
updater = self.helper.updater
updater.update_qc_fig(self.initialization_values["dataset_filter"],
self.initialization_values["sop_exp_qc_tab_filter"],
self.initialization_values["material_filter"],
self.initialization_values["issue_filter"],
self.initialization_values["qc_tab_dataset_qc_status_filter"])
qc_fig = gr.Plot(elem_id="plot_wScrollBar",
value=updater.qc_visual)
qc_fig_no_file_msg = gr.Markdown(
value="All files are filtered", visible=False)
return qc_fig, qc_fig_no_file_msg
def analysis_tab_filter_components(self):
gr.Markdown("Sample")
sample_filter = gr.Dropdown(label='',
choices=self.helper.choices["sample_filter"],
value=self.helper.choices["sample_filter"][0])
self.initialization_values["sample_filter"] = self.helper.choices["sample_filter"][0]
gr.Markdown("SOP-Exp")
sop_exp_analysis_tab_filter = gr.Dropdown(label='',
choices=self.helper.choices["sop_exp_analysis_tab_filter"],
value=self.helper.choices["sop_exp_analysis_tab_filter"][0])
self.initialization_values["sop_exp_analysis_tab_filter"] = self.helper.choices["sop_exp_analysis_tab_filter"][0]
gr.Markdown("Compensation control")
compensation_control_filter = gr.Dropdown(label='',
choices=self.helper.choices["compensation_control_filter"],
value=self.helper.choices["compensation_control_filter"][0])
self.initialization_values["compensation_control_filter"] = self.helper.choices["compensation_control_filter"][0]
gr.Markdown("Gating control")
gating_control_filter = gr.Dropdown(label='',
choices=self.helper.choices["gating_control_filter"],
value=self.helper.choices["gating_control_filter"][0])
self.initialization_values["gating_control_filter"] = self.helper.choices["gating_control_filter"][0]
gr.Markdown("Population; Phenotype (Parent gate)")
pop_pheno_parent_filter = gr.Dropdown(label='',
choices=self.helper.choices["pop_pheno_parent_filter"],
value=self.helper.choices["pop_pheno_parent_filter"][3])
self.initialization_values["pop_pheno_parent_filter"] = self.helper.choices["pop_pheno_parent_filter"][3]
clear_gating_tab_filters_button = gr.Button(
value="Clear selections")
analysis_tab_apply_filters_button = gr.Button(
value="Apply filters")
return sample_filter, sop_exp_analysis_tab_filter, compensation_control_filter, \
gating_control_filter, pop_pheno_parent_filter, clear_gating_tab_filters_button, analysis_tab_apply_filters_button
def analyzed_result_filter_component(self):
gr.Markdown("Dataset QC status")
analysis_tab_dataset_qc_status_filter = gr.CheckboxGroup(label='',
choices=[
"Pass QC", "Fail QC"],
value=["Pass QC"])
self.initialization_values["analysis_tab_dataset_qc_status_filter"] = [
"Pass QC"]
gr.Markdown("Analyzed result")
analyzed_result_filter = gr.Dropdown(label='',
multiselect=True,
choices=self.helper.config["gating_results_tab"]["results"],
value=[self.helper.config["gating_results_tab"]["results"][0]])
self.initialization_values["analyzed_result_filter"] = [
self.helper.config["gating_results_tab"]["results"][0]]
return analysis_tab_dataset_qc_status_filter, analyzed_result_filter
def analysis_barplot_components(self):
updater = self.helper.updater
updater.update_barplot_fig(self.initialization_values["dataset_filter"],
self.initialization_values["analysis_tab_dataset_qc_status_filter"],
self.initialization_values["sample_filter"],
self.initialization_values["sop_exp_analysis_tab_filter"],
self.initialization_values["compensation_control_filter"],
self.initialization_values["gating_control_filter"],
self.initialization_values["pop_pheno_parent_filter"],
self.initialization_values["analyzed_result_filter"])
analysis_barplot_fig = gr.Plot(elem_id="plot_wScrollBar",
value=updater.barplot)
barplot_not_reportable_msg = gr.Markdown(
value="The assigned result type of the experiment is not reportable.", visible=False)
return analysis_barplot_fig, barplot_not_reportable_msg
def analysis_heatmap_components(self):
gr.Markdown("Protocol for multi-exps comparison")
compared_protocol_filter = gr.Radio(label='',
choices=[
"Compensation control", "Population; Phenotype (Parent gate)"],
value="Compensation control")
include_CDA_results_checkbox = gr.Checkbox(label="Include available CDA results",
value=False)
updater = self.helper.updater
updater.update_heatmap_fig(self.initialization_values["dataset_filter"],
self.initialization_values["analysis_tab_dataset_qc_status_filter"],
self.initialization_values["sample_filter"],
self.initialization_values["sop_exp_analysis_tab_filter"],
self.initialization_values["compensation_control_filter"],
self.initialization_values["gating_control_filter"],
self.initialization_values["pop_pheno_parent_filter"],
self.initialization_values["analyzed_result_filter"],
"Compensation control",
False)
with gr.Row():
analysis_heatmap_exp_info_table = gr.Dataframe(
show_copy_button=True, value=updater.exp_info_table)
analysis_heatmap_exp_comparison_table = gr.Dataframe(
show_copy_button=True, value=updater.exp_comparison_table)
analysis_heatmap_fig = gr.Plot(elem_id="plot_wScrollBar",
value=updater.heatmap)
heatmap_not_reportable_msg = gr.Markdown(
value="The assigned result type of all compared experiments are not reportable.", visible=False)
return compared_protocol_filter, include_CDA_results_checkbox, analysis_heatmap_exp_info_table, analysis_heatmap_exp_comparison_table, \
analysis_heatmap_fig, heatmap_not_reportable_msg
class _Updater:
def __init__(self,
helper_instance):
self.helper = helper_instance
def select_all_choices(self, filter_name: str):
return gr.update(value=self.helper.choices[filter_name])
def clean_all_choices(self):
return gr.update(value=[])
def update_dataset_filter(self, selected_models: list[str], selected_datasets: list[str]):
model_dataset_mapping = self.helper.file_qc_results.groupby("Instrument model")["Dataset"].apply(
lambda x: sorted(x.unique().tolist())).to_dict()
updated_dataset_choices = set()
if selected_models:
for selected_model in selected_models:
if selected_model in model_dataset_mapping.keys():
updated_dataset_choices.update(
model_dataset_mapping[selected_model])
updated_dataset_choices = sorted(
list(updated_dataset_choices))
self.helper.choices["dataset_filter"] = updated_dataset_choices
updated_dataset_values = [
item for item in selected_datasets if item in updated_dataset_choices]
return gr.update(choices=updated_dataset_choices,
value=updated_dataset_values)
def update_material_filter(self, selected_sop_exps: list[str], selected_materials: list[str]):
df = self.helper.file_qc_results.copy()
df["Material"] = df["SOP-Exp"] + " " + df["Material"]
sop_exp_material_mapping = df.groupby("SOP-Exp")["Material"].apply(
lambda x: sorted(x.unique().tolist())).to_dict()
updated_material_choices = set()
if selected_sop_exps:
for selected_sop_exp in selected_sop_exps:
if selected_sop_exp in sop_exp_material_mapping.keys():
updated_material_choices.update(
sop_exp_material_mapping[selected_sop_exp])
updated_material_choices = sorted(
list(updated_material_choices))
self.helper.choices["material_filter"] = updated_material_choices
updated_material_values = [
item for item in selected_materials if item in updated_material_choices]
return gr.update(choices=updated_material_choices,
value=updated_material_values)
def _filter_dataset_qc_result(self, selected_datasets, selected_dataset_qc_status):
return self.helper.dataset_qc_results[(self.helper.dataset_qc_results["Dataset"].isin(selected_datasets)) &
(self.helper.dataset_qc_results["QC status"].isin(selected_dataset_qc_status))]
def update_file_qc_table(self,
selected_datasets: list[str], selected_sop_exps: list[str], selected_materials: list[str],
selected_qc_issues: list[str], selected_dataset_qc_status: list[str], selected_file_qc_status: list[str]):
self.filtered_dataset_qc_results = self._filter_dataset_qc_result(
selected_datasets, selected_dataset_qc_status)
selected_materials = [material.split(
" ")[1] for material in selected_materials]
self.filtered_file_qc_results = self.helper.file_qc_results.loc[(self.helper.file_qc_results["Dataset"].isin(self.filtered_dataset_qc_results["Dataset"])) &
(self.helper.file_qc_results["SOP-Exp"].isin(selected_sop_exps)) &
(self.helper.file_qc_results["Material"].isin(selected_materials)) &
(self.helper.file_qc_results["QC status"].isin(
selected_file_qc_status)),
self.helper.config["qc_results_tab"]["file_infos"]+selected_qc_issues]
if len(self.filtered_file_qc_results) == 0:
file_qc_table_update = {"visible": False}
file_qc_table_no_file_msg_update = {"visible": True}
else:
file_qc_table_update = {
"value": {"data": self.filtered_file_qc_results.values.tolist(),
"headers": self.filtered_file_qc_results.columns.to_list()},
"visible": True}
file_qc_table_no_file_msg_update = {"visible": False}
if len(self.filtered_dataset_qc_results) == 0:
dataset_qc_table_update = {"visible": False}
dataset_qc_table_no_dataset_msg_update = {
"visible": True}
else:
dataset_qc_table_update = {
"value": {"data": self.filtered_dataset_qc_results.values.tolist(),
"headers": self.filtered_dataset_qc_results.columns.to_list()},
"visible": True}
dataset_qc_table_no_dataset_msg_update = {
"visible": False}
return [gr.update(**file_qc_table_update),
gr.update(**file_qc_table_no_file_msg_update),
gr.update(**dataset_qc_table_update),
gr.update(**dataset_qc_table_no_dataset_msg_update)]
def update_qc_fig(self,
selected_datasets: list[str], selected_sop_exps: list[str], selected_materials: list[str],
selected_qc_issues: list[str], selected_dataset_qc_status: list[str]):
filtered_dataset_qc_results = self.filtered_dataset_qc_results = self._filter_dataset_qc_result(
selected_datasets, selected_dataset_qc_status)
selected_materials = [material.split(
" ")[1] for material in selected_materials]
filtered_file_qc_results = self.helper.file_qc_results.loc[(self.helper.file_qc_results["Dataset"].isin(filtered_dataset_qc_results["Dataset"])) &
(self.helper.file_qc_results["SOP-Exp"].isin(selected_sop_exps)) &
(self.helper.file_qc_results["Material"].isin(
selected_materials)),
self.helper.config["qc_results_tab"]["file_infos"]+selected_qc_issues]
if len(filtered_file_qc_results) == 0:
return [gr.update(visible=False), gr.update(visible=True)]
else:
self.qc_visual = QCVisualizer.visualize(filtered_dataset_qc_results,
self.helper.config["qc_results_tab"]["file_sets"],
filtered_file_qc_results,
selected_qc_issues)
return [gr.update(value=self.qc_visual,
visible=True),
gr.update(visible=False)]
def update_barplot_fig(self,
selected_datasets: list[str], selected_dataset_qc_status: list[str],
selected_sample, selected_sop_exp, selected_comp,
selected_fmo, selected_pop_pheno_parent, selected_results: list[str]):
if any([s is None for s in [selected_sample, selected_sop_exp, selected_comp, selected_fmo, selected_pop_pheno_parent]]):
return [gr.update(visible=False), gr.update(visible=False)]
filtered_dataset_qc_results = self.filtered_dataset_qc_results = self._filter_dataset_qc_result(
selected_datasets, selected_dataset_qc_status)
filtered_analyzed_gating_results = self.helper.analyzed_gating_results.loc[(self.helper.analyzed_gating_results["Dataset"].isin(filtered_dataset_qc_results["Dataset"])) &
(self.helper.analyzed_gating_results["Sample"] == selected_sample) &
(self.helper.analyzed_gating_results["SOP-Exp"] == selected_sop_exp) &
(self.helper.analyzed_gating_results["Compensation control"] == selected_comp) &
(self.helper.analyzed_gating_results["Gating control"] == selected_fmo) &
(self.helper.analyzed_gating_results["Population; Phenotype (Parent gate)"] == selected_pop_pheno_parent),
self.helper.config["gating_results_tab"]["exp_infos"]+[c for c in self.helper.analyzed_gating_results.columns if any(selected_result in c for selected_result in selected_results)]]
# if (filtered_analyzed_gating_results[[f"{selected_result}_mean", f"{selected_result}_std"]] == "Not reportable").all(axis=None):
# return [gr.update(visible=False), gr.update(visible=True)]
self.barplot = AnalysisVisualizer.visualize_barplot(filtered_analyzed_gating_results,
selected_results)
return [gr.update(value=self.barplot,
visible=True),
gr.update(visible=False)]
def update_heatmap_fig(self,
selected_datasets: list[str], selected_dataset_qc_status: list[str],
selected_sample, selected_sop_exp, selected_comp,
selected_fmo, selected_pop_pheno_parent, selected_results: list[str], selected_comparison,
include_CDA):
if include_CDA:
analyzed_results = self.helper.analyzed_gating_results_wCDAResults
else:
analyzed_results = self.helper.analyzed_gating_results
if any([s is None for s in [selected_sample, selected_sop_exp, selected_comp, selected_fmo, selected_pop_pheno_parent]]):
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]
masks = {"Sample": analyzed_results["Sample"] == selected_sample,
"SOP-Exp": analyzed_results["SOP-Exp"] == selected_sop_exp,
"Compensation control": analyzed_results["Compensation control"] == selected_comp,
"Gating control": analyzed_results["Gating control"] == selected_fmo,
"Population; Phenotype (Parent gate)": analyzed_results["Population; Phenotype (Parent gate)"] == selected_pop_pheno_parent}
filtering_mask = pd.DataFrame(pd.concat([mask for mask_name, mask in masks.items(
) if mask_name != selected_comparison], axis=1)).all(axis=1)
filtered_dataset_qc_results = self.filtered_dataset_qc_results = self._filter_dataset_qc_result(
selected_datasets, selected_dataset_qc_status)
filtered_analyzed_gating_results = analyzed_results.loc[analyzed_results["Dataset"].isin(filtered_dataset_qc_results["Dataset"]) &
filtering_mask,
self.helper.config["gating_results_tab"]["exp_infos"]+[c for c in analyzed_results.columns if any(selected_result in c for selected_result in selected_results)]]
# if (filtered_analyzed_gating_results[[f"{selected_result}_mean", f"{selected_result}_std"]] == "Not reportable").all(axis=None):
# return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
self.exp_info_table = filtered_analyzed_gating_results[[
e for e in self.helper.config["gating_results_tab"]["exp_infos"] if e not in ["Dataset", "Site (anonymized)", "Instrument model", "Result ID"]]]
if selected_comparison == "Population; Phenotype (Parent gate)":
shown_exp_comparison = [
"Population", "Phenotype", "Parent gate"]
else:
shown_exp_comparison = [selected_comparison]
self.exp_info_table = pd.DataFrame(self.exp_info_table).drop(
columns=shown_exp_comparison)
self.exp_info_table = self.exp_info_table.value_counts().reset_index().drop(
columns="count").T.reset_index().set_axis(["Protocol", "Content"], axis=1)
self.exp_comparison_table = filtered_analyzed_gating_results[["Result ID"]+shown_exp_comparison].value_counts(
).reset_index().drop(columns="count")
if selected_comparison == "Population; Phenotype (Parent gate)":
filtered_analyzed_gating_results["Result ID"] = filtered_analyzed_gating_results["Result ID"].astype(str) + \
" (" + filtered_analyzed_gating_results["Population"] + ")"
else:
filtered_analyzed_gating_results["Result ID"] = filtered_analyzed_gating_results["Result ID"].astype(str) + \
" (" + filtered_analyzed_gating_results[selected_comparison] + ")"
self.heatmap = AnalysisVisualizer.visualize_heatmap(filtered_analyzed_gating_results,
selected_results)
return [gr.update(value=self.exp_info_table, visible=True),
gr.update(value=self.exp_comparison_table, visible=True),
gr.update(value=self.heatmap,
visible=True),
gr.update(visible=False)]
def update_analysis_tab_filters(self, selected_sample, selected_sop_exp, selected_comp, selected_fmo, selected_pop_pheno_parent):
masks = {}
col_selection_mapping = dict(zip(["Sample", "SOP-Exp", "Compensation control", "Gating control", "Population; Phenotype (Parent gate)"],
[selected_sample, selected_sop_exp, selected_comp, selected_fmo, selected_pop_pheno_parent]))
for col, selection in col_selection_mapping.items():
if selection is not None:
masks[col] = self.helper.analyzed_gating_results[col] == selection
if len(masks) == 0:
return self.clear_analysis_tab_filters()
filtering_mask = pd.DataFrame(
pd.concat([mask for mask in masks.values()], axis=1)).all(axis=1)
filtered_analyzed_gating_results = self.helper.analyzed_gating_results[
filtering_mask]
updated_choices = {}
updated_values = {}
for col in ["Sample", "SOP-Exp", "Compensation control", "Gating control", "Population; Phenotype (Parent gate)"]:
updated_choices[col] = sorted(
list(filtered_analyzed_gating_results[col].unique()))
updated_values[col] = (
col_selection_mapping[col] if col_selection_mapping[col] in updated_choices[col] else None)
return [gr.update(choices=updated_choices[col], value=updated_values[col])
for col in ["Sample", "SOP-Exp", "Compensation control", "Gating control", "Population; Phenotype (Parent gate)"]]
def clear_analysis_tab_filters(self):
return [gr.update(choices=self.helper.choices["sample_filter"], value=None),
gr.update(
choices=self.helper.choices["sop_exp_analysis_tab_filter"], value=None),
gr.update(
choices=self.helper.choices["compensation_control_filter"], value=None),
gr.update(
choices=self.helper.choices["gating_control_filter"], value=None),
gr.update(choices=self.helper.choices["pop_pheno_parent_filter"], value=None)]
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