Dash: How to select data points either in the table which updates the plot and on the graph which updates the selection on the table?

I am new on dash and plotly solutions. I am wondering if it is possible to join both approaches in the same application. Selecting the points, update the checkbox and then unselect some checkbox updating the row color. I have this code that i found here: https://community.plotly.com/t/dash-how-to-select-data-points-either-in-the-table-which-updates-the-plot-or-on-the-graph-which-updates-the-selection-on-the-table/46674, but I don't know how to do. Thanks.

from dash import dcc, html, dash_table
from dash.dependencies import Input, Output, State
import dash
import dash_bootstrap_components as dbc
import json
import pandas as pd
import plotly.graph_objects as go

df = pd.DataFrame.from_dict(
    {'term': {0: 'GOCC:0043229', 1: 'GOCC:0098588', 2: 'GOCC:0005730', 3: 'GO:0005730', 4: 'GO:0005783', 5: 'GO:0031410', 6: 'KW-0732', 7: 'KW-0156', 8: 'KW-0010'},
     'description': {0: 'Intracellular organelle', 1: 'Bounding membrane of organelle', 2: 'Nucleolus', 3: 'nucleolus', 4: 'endoplasmic reticulum', 5: 'cytoplasmic vesicle', 6: 'Signal', 7: 'Chromatin regulator', 8: 'Activator'},
     'FG_count': {0: 370, 1: 92, 2: 126, 3: 500, 4: 63, 5: 23, 6: 9, 7: 410, 8: 31},
     'logFDR': {0: 2.6, 1: 4, 2: 5, 3: 2, 4: 7, 5: 8, 6: 5, 7: 1, 8: 6},
     'effectSize': {0: 0.053, 1: -0.049, 2: 0.046, 3: 0.025, 4: -0.040, 5: -0.027, 6: -0.024, 7: 0.025, 8: 0.047},
     'category': {0: 'TM', 1: 'TM', 2: 'TM', 3: 'GOCC', 4: 'GOCC', 5: 'GOCC', 6: 'UPK', 7: 'UPK', 8: 'UPK'}})

style_data_conditional_basic = [{"if": {"state": "selected"}, "backgroundColor": "gold", "border": "inherit !important", "text_align": "inherit !important", }] + [
    {"if": {"state": "active"}, "backgroundColor": "inherit !important", "border": "inherit !important", "text_align": "inherit !important", }]

app = dash.Dash(__name__, prevent_initial_callbacks=True,
                external_stylesheets=[dbc.themes.BOOTSTRAP])
marker_size_min_ = 4
sizeref = 0.05

data_table_ex = dash_table.DataTable(
    id='main_datatable',
    columns=[{"name": colName, "id": colName} for colName in df.columns],
    data=df.to_dict('records'),
    sort_action="native",
    row_selectable="multi",
    selected_columns=[],
    selected_rows=[],
    style_as_list_view=True,
    style_data={'if': {'row_index': 'odd'}, 'backgroundColor': "#F5F5F5", },
    style_data_conditional=[],
    style_cell={'minWidth': "10px", "width": "50px", "maxWidth": "80px", "fontSize": "12px", "font-family": "sans-serif", "text_align": "center", "border": "1px", },)


def create_scatter_plot_graph(df):
    fig = go.Figure()
    for category_name, group in df.groupby("category"):
        fig.add_trace(go.Scatter(name=category_name, x=group["logFDR"].tolist(), y=group["effectSize"].tolist(), ids=group["term"].tolist(), legendgroup=category_name, mode="markers", marker_symbol="circle", marker_size=group["FG_count"], marker_sizemin=marker_size_min_,
                      marker_sizemode="area", marker_sizeref=sizeref, customdata=[list(ele) for ele in zip(group["term"], group["description"], group["FG_count"])], hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>Size: %{customdata[2]}<extra></extra>", ))
    fig.update_layout(xaxis={'title': 'log(FDR)'},
                      yaxis={'title': 'effect size'})
    scatter_plot_graph = dcc.Graph(id='scatter_plot', figure=fig)
    return scatter_plot_graph


scatter_plot_graph = create_scatter_plot_graph(df)

app.layout = html.Div(id='general_div', className="container-fluid",
                      children=[
                          dbc.Row([
                              dbc.Col(
                                  html.Div(id="scatter_container", children=[scatter_plot_graph]), xs={"size": 12}, sm={"size": 12}, md={"size": 10}, lg={"size": 8}, ),
                          ], justify="center", ),
                          html.Br(),
                          dbc.Row([
                              dbc.Col(
                                  html.Div(data_table_ex), xs={"size": 12}, sm={"size": 12}, md={"size": 10}, lg={"size": 10},),
                          ], justify="center",),
                          html.Br(),
                      ])


@app.callback([
    Output(component_id="main_datatable",
           component_property="style_data_conditional"),
    Output(component_id="scatter_container", component_property="children")
],
    [
        Input(component_id="main_datatable",
              component_property="selected_rows"),
        Input(component_id="main_datatable",
              component_property="derived_virtual_data"),
        Input(component_id="main_datatable",
              component_property='derived_virtual_selected_rows')
])
def on_selectInDataTable_highlight_dataTableRows_and_pointsInScatterPlot(selected_rows, derived_virtual_data, derived_virtual_selected_rows):
    dff = df if len(derived_virtual_data) == 0 else pd.DataFrame(
        derived_virtual_data)
    dff["marker_line_width"] = 1
    dff["marker_line_color"] = "white"
    dff.loc[derived_virtual_selected_rows, "marker_line_width"] = 4
    dff.loc[derived_virtual_selected_rows, "marker_line_color"] = "black"
    fig = go.Figure()
    for category_name, group in dff.groupby("category"):
        fig.add_trace(go.Scatter(name=category_name, x=group["logFDR"].tolist(), y=group["effectSize"].tolist(), ids=group["term"].tolist(), legendgroup=category_name, mode="markers", marker_symbol="circle", marker_size=group["FG_count"], marker_sizemin=marker_size_min_, marker_sizemode="area", marker_sizeref=sizeref,
                      marker_line_width=group["marker_line_width"], marker_line_color=group["marker_line_color"], customdata=[list(ele) for ele in zip(group["term"], group["description"], group["FG_count"])], hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>Size: %{customdata[2]}<extra></extra>", ))
    fig.update_layout(xaxis={'title': 'log(FDR)'},
                      yaxis={'title': 'effect size'})
    scatter_plot_fig = dcc.Graph(id='scatter_plot', figure=fig)

    if selected_rows is not None:
        selected_term_list = dff.loc[selected_rows, "term"].tolist()
        style_data_conditional_extension = [{'if': {'filter_query': '{term}=' + "{}".format(
            term)}, 'backgroundColor': 'gold'} for term in selected_term_list]
        return style_data_conditional_extension + style_data_conditional_basic, scatter_plot_fig
    else:
        return style_data_conditional_basic, scatter_plot_fig


@app.callback([
    Output(component_id="main_datatable",
           component_property="style_data_conditional"),
    Output(component_id="main_datatable", component_property="selected_rows")
],
    [
        Input(component_id="scatter_plot", component_property="selectedData")
])
def on_selectInScatter_highlight_and_select_dataTableRows(selectedData):
    selected_term_list, selected_rows = [], selectedData
    if selectedData is not None:
        for point in selectedData["points"]:
            selected_term_list.append(point["customdata"][0])
        style_data_conditional_extension = [{'if': {'filter_query': '{term}='+"{}".format(
            term)}, 'backgroundColor': 'gold'} for term in selected_term_list]
        selected_rows = df[df["term"].isin(selected_term_list)].index.tolist()
        return style_data_conditional_extension + style_data_conditional_basic, selected_rows
    return style_data_conditional_basic, selected_rows


if __name__ == '__main__':
    app.run_server(debug=True)

You should combine the two callbacks into one, using dash.callback_context to flow-control as explained in the documentation here.

As possible solution:

import dash

@app.callback(
    [
        Output(component_id="main_datatable", component_property="style_data_conditional"),
        Output(component_id="scatter_container", component_property="children"),
        Output(component_id="main_datatable", component_property="selected_rows")
    ],
    [
        Input(component_id="main_datatable", component_property="selected_rows"),
        Input(component_id="main_datatable", component_property="derived_virtual_data"),
        Input(component_id="main_datatable", component_property='derived_virtual_selected_rows'),
        Input(component_id="scatter_plot", component_property="selectedData")
    ],
    [
        State(component_id="scatter_container", component_property="children"),
        State(component_id="main_datatable", component_property="selected_rows")
    ]
)
def update_by_trigger(selected_rows, derived_virtual_data, derived_virtual_selected_rows, selectedData, current_scatter, current_rows):
    
    ctx = dash.callback_context

    # Check what triggered the update
    if not ctx.triggered:
        return dash.no_update
    else: 
        trigger = ctx.triggered[0]["prop_id"].split(".")[0]

    
    if trigger == "main_datatable":

        style_data_conditional_basic, scatter_plot_fig = on_selectInDataTable_highlight_dataTableRows_and_pointsInScatterPlot(selected_rows, derived_virtual_data, derived_virtual_selected_rows)
        return style_data_conditional_basic, scatter_plot_fig, current_rows

    
    elif trigger == "scatter_plot":
        style_data_conditional_basic, selected_rows =  on_selectInScatter_highlight_and_select_dataTableRows(selectedData)
        return style_data_conditional_basic, current_scatter, selected_rows

    else:
        return dash.no_update
    
# Old callbacks as function to call in the combined callback
def on_selectInDataTable_highlight_dataTableRows_and_pointsInScatterPlot(selected_rows, derived_virtual_data, derived_virtual_selected_rows):
    dff = df if len(derived_virtual_data) == 0 else pd.DataFrame(
        derived_virtual_data)
    dff["marker_line_width"] = 1
    dff["marker_line_color"] = "white"
    dff.loc[derived_virtual_selected_rows, "marker_line_width"] = 4
    dff.loc[derived_virtual_selected_rows, "marker_line_color"] = "black"
    fig = go.Figure()
    for category_name, group in dff.groupby("category"):
        fig.add_trace(go.Scatter(name=category_name, x=group["logFDR"].tolist(), y=group["effectSize"].tolist(), ids=group["term"].tolist(), legendgroup=category_name, mode="markers", marker_symbol="circle", marker_size=group["FG_count"], marker_sizemin=marker_size_min_, marker_sizemode="area", marker_sizeref=sizeref,
                      marker_line_width=group["marker_line_width"], marker_line_color=group["marker_line_color"], customdata=[list(ele) for ele in zip(group["term"], group["description"], group["FG_count"])], hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>Size: %{customdata[2]}<extra></extra>", ))
    fig.update_layout(xaxis={'title': 'log(FDR)'},
                      yaxis={'title': 'effect size'})
    scatter_plot_fig = dcc.Graph(id='scatter_plot', figure=fig)

    if selected_rows is not None:
        selected_term_list = dff.loc[selected_rows, "term"].tolist()
        style_data_conditional_extension = [{'if': {'filter_query': '{term}=' + "{}".format(
            term)}, 'backgroundColor': 'gold'} for term in selected_term_list]
        return style_data_conditional_extension + style_data_conditional_basic, scatter_plot_fig
    else:
        return style_data_conditional_basic, scatter_plot_fig


def on_selectInScatter_highlight_and_select_dataTableRows(selectedData):
    selected_term_list, selected_rows = [], selectedData
    if selectedData is not None:
        for point in selectedData["points"]:
            selected_term_list.append(point["customdata"][0])
        style_data_conditional_extension = [{'if': {'filter_query': '{term}='+"{}".format(
            term)}, 'backgroundColor': 'gold'} for term in selected_term_list]
        selected_rows = df[df["term"].isin(selected_term_list)].index.tolist()
        return style_data_conditional_extension + style_data_conditional_basic, selected_rows
    return style_data_conditional_basic, selected_rows