Forums

import dash
from dash import html, dcc, callback, Input, Output, State
import pandas as pd
import plotly.express as px
from sklearn.preprocessing import StandardScaler
import plotly.graph_objs as go
import numpy as np

boxplot_fig = px.box(melted_data,
                     y='variable',
                     x='value',
                     title='Boxplot of Clinical Attributes Distribution(Standardized)',
                     orientation='h',  # Set orientation to horizontal (vertical boxplot)
                     )

start_column_index = 520

# Select the subset of columns from 'start_column_index' to the end
selected_columns = data.iloc[:, start_column_index:]

# Count non-zero occurrences for each gene in the mutation data
gene_mutation_count = pd.Series([np.sum(selected_columns[col].astype(str).str.strip() != '0') for col in selected_columns], index=selected_columns.columns)

gene = gene_mutation_count.index.str.replace('_mut', '')
val = gene_mutation_count.values
gene_mutation_count_df = pd.DataFrame({'Gene Name': gene, 'Mutation Count': val})
gene_mutation_fig = px.bar(gene_mutation_count_df, x='Gene Name', y='Mutation Count',
             title='Gene Mutation Distribution',
             labels={'Gene Name': 'Gene Name', 'Mutation Count': 'Mutation Count'}
            )

layout = html.Div(children=[

    html.Div(className='row', children=[
        html.Div(className='six columns', children=[
            dcc.Graph(id='boxplot', figure=boxplot_fig)
        ]),
        html.Div(className='six columns', children=[
            dcc.Graph(id='gene-mutation-distplot', figure=gene_mutation_fig)
        ])
    ]), ...